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What is Artificial Empathy? How Will it Impact AI?

What is Artificial Empathy? How Will it Impact AI?

Artificial Intelligence (AI) can feel far from what it means to be human. It mostly focuses on thinking clearly and working efficiently. As we use technology more every day, we want machines to talk to us in a way that feels natural and kind. Artificial empathy is a new field aiming to close this gap. This part of AI helps machines understand and respond to human emotions, enhancing AI Services like virtual assistants, customer support, and personalized recommendations. This way, our interactions feel more real and friendly, improving the overall user experience with AI-driven services.

Imagine chatting with a customer help chatbot that understands your frustration. It stays calm and acknowledges your feelings. It offers you comfort. This is how artificial empathy works. It uses smart technology to read and respond to human emotions. This makes your experience feel more friendly and relaxing.

Highlights:

  • Artificial empathy helps AI understand how people feel and respond to their emotions.
  • By mixing psychology, language skills, and AI, artificial empathy makes human-machine interactions feel more natural.
  • It can change how we work in areas like customer service, healthcare, and education.
  • There are big concerns about data safety, misuse of the technology, and making fair rules.
  • Artificial empathy aims to support human feelings, not take their place, to improve our connection with technology.

What is Artificial Empathy?

Artificial empathy is a type of AI designed to notice and respond to human feelings. Unlike real empathy, where people feel emotions, artificial empathy means teaching machines to read emotional signals and provide fitting responses. This makes machines seem caring, even though they do not feel emotions themselves.

For example, an AI chatbot can see words like, “I’m so frustrated,” and understand that the person is unhappy. It can respond with a warm message like, “I’m here to help you. Let’s work on this together.” Even though the AI does not feel compassion, its reply makes the chat seem more supportive and useful for the user.

How Does Artificial Empathy Work?

Developing artificial empathy takes understanding feelings and clever programming. Here’s how it works, step by step:

  • Recognizing Emotions: AI systems use face recognition tools to read feelings by looking at expressions. A smile often shows happiness, and a frown usually means sadness or frustration.
    • Tone analysis helps AI detect feelings in speech. A loud and sharp voice might mean anger, while a soft, careful voice may show sadness.
    • Sentiment analysis looks at the words we say. If someone says, “I’m really annoyed,” the AI identifies a negative feeling and changes how it responds.
  • Interpreting Emotional Cues: After spotting an emotional state, the AI thinks about what it means in the conversation. This is important because feelings can be complex, and the same word or expression might have different meanings based on the situation.
  • Responding Appropriately: Once the AI understands how the user feels, it chooses a response that matches the mood. If it sees frustration, it might offer help or provide clearer solutions.
    • Over time, AI can learn from past conversations and adjust its replies, getting better at showing human-like empathy.

AI is getting better at seeing and understanding emotions because of machine learning. It learns from a lot of data about how people feel. With each chat, it gets better at replying. This helps make future conversations feel more natural.

Technologies Enabling Artificial Empathy

Several new technologies work together to create artificial empathy.

  • Facial Recognition Software: This software examines facial expressions to understand how a person feels. It can tell a real smile, where the eyes crinkle, from a polite or “fake” smile that only uses the mouth.
    • This software is often used in customer service and healthcare. Knowing emotions can help make interactions better.
  • Sentiment Analysis: Sentiment analysis looks at words to understand feelings. By examining various words and phrases, AI can see if someone is happy, angry, or neutral.
    • This tool is crucial for watching social media and checking customer feedback. Understanding how people feel can help companies respond to what customers want.
  • Voice Tone Analysis: Voice analysis helps AI feel emotions based on how words are spoken, like tone, pitch, and speed. This is often used in call centers, where AI can sense if a caller is upset. This helps link the caller to a live agent quickly for better support.
  • Natural Language Processing (NLP): NLP allows AI to understand language patterns and adjust its replies. It can tell sarcasm and notice indirect ways people show emotions, making conversations feel smoother and more natural.

Each of these technologies has a specific job. Together, they help AI understand and respond to human feelings.

Real-World Applications of Artificial Empathy

1. Customer Service:

  • In customer support, pretending to care can really improve user experiences. For instance, imagine calling a helpline and talking to an AI helper. If the AI notices that you sound upset, it might say, “I’m sorry you’re having a tough time. Let me help you fix this quickly.”
  • Such a caring reply helps calm users and can create a good outcome for both the customer and the support team.

2. Healthcare:

  • In healthcare, AI that can show understanding helps patients by noticing their feelings. This is very useful in mental health situations. For example, an AI used in therapy apps can tell if a user sounds sad. It can then respond with support or helpful tips.
  • Also, this caring AI can help doctors find mood problems. It does this by looking at facial expressions, voice tones, and what people say. For example, AI might notice signs of being low or stressed in a person’s voice. This gives important details to mental health experts.

3. Education:

  • In education, artificial empathy can help make learning feel more personal. If a student looks confused or upset while using an online tool, AI can notice this. It can then adjust the lesson to be easier or offer encouragement. This makes the experience better and more engaging.
  • AI tutors that show empathy can provide feedback based on how a student feels. This helps keep their motivation high and makes them feel good even in difficult subjects.

4. Social Media and Online Safety:

  • AI that can read feelings can find bad interactions online, like cyberbullying or harassment. By spotting negative words, AI can report the content and help make online places safer.
  • If AI sees harmful words directed at someone, it can tell moderators or provide support resources to that person.

Benefits of Artificial Empathy

The growth of artificial empathy has several benefits:

  • Better User Experiences: Friendly AI makes conversations feel more engaging and enjoyable. When users feel understood, they are more likely to trust and use AI tools.
  • More Care: In healthcare, friendly AI can meet patients’ emotional needs. This helps create a more caring environment. In customer service, it can help calm tense situations by showing empathy.
  • Smart Interaction Management: AI systems that recognize emotions can handle calls and messages more effectively. They can adjust their tone or words and pass chats to human agents if needed.
  • Helping Society: By detecting signs of stress or anger online, AI can help create safer and friendlier online spaces.

Ethical Concerns and Challenges

While artificial empathy has many benefits, it also raises some ethical questions.

  • Data Privacy: Empathetic AI needs to use personal data, like voice tone and text messages. We must have strict privacy rules to keep users safe when handling this kind of information.
  • Transparency and Trust: Users should know when they talk with empathetic AI and see how their data is used. Clear communication helps build trust and makes users feel secure.
  • Risk of Manipulation: Companies might use empathetic AI to influence people’s choices unfairly. For example, if AI notices a user is sad, it might suggest products to help them feel better. This could be a worry because users may not see it happening.
  • Fairness and Bias: AI can only be fair if it learns from fair data. If the data has bias, empathetic AI might not get feelings right or treat some groups differently. It’s very important to train AI with a variety of data to avoid these problems.
  • Too Much Dependence on Technology: If people depend too much on empathetic AI for emotional support, it could harm real human connections. This might result in less real empathy in society.

Navigating Privacy and Ethical Issues

To fix these problems, developers need to be careful.

  • Data Security Measures: Strong encryption and anonymizing data can help protect private emotional information.
  • Transparency with Users: People should know what data is collected and why. Clear consent forms and choices to opt-out can help users manage their information.
  • Bias Testing and Fixing: Regular testing and using different training data can help reduce bias in AI. We should keep improving algorithms for fair and right responses.
  • Ethical Guidelines and Standards: Following guidelines can help ensure AI development matches community values. Many groups are creating standards for AI ethics, focusing on user care and responsibility.

The Future of Artificial Empathy

Looking forward, added empathy in AI can help people connect better with it. Future uses may include:

  • AI Companions: In the future, friendly AIs could be digital friends. They would provide support and companionship to people who feel lonely or need help.
  • Healthcare Helpers: Caring AIs could play a bigger role in healthcare. They would offer emotional support to elderly people, those with disabilities, and anyone dealing with mental health issues.
  • Education and Personalized Learning: As AIs get better at recognizing how students feel, they can change lessons to match each person’s emotions. This would make learning more fun and enjoyable.

As artificial empathy increases, we must think about ethics. We need to care about people’s well-being and respect their privacy. By doing this, we can use AI to build better, kinder connections.

Conclusion

Artificial empathy can change how we use AI. It can make it feel friendlier and better connected to our feelings. This change offers many benefits in areas like customer service, healthcare, and education. However, we need to be careful about ethical concerns. These include privacy, being clear about how things work, and the risk of unfair treatment.

Empathetic AI can link technology and real human emotions. It helps us feel more supported when we use technology. In the future, we need to grow this kind of artificial empathy responsibly. It should align with our values and support what is right for society. By accepting the potential of artificial empathy, we can create a world where AI helps us and understands our feelings. This will lead to a kinder use of technology. Codoid provides the best AI services, ensuring that artificial empathy is developed with precision and aligns with ethical standards, enhancing user experiences and fostering a deeper connection between technology and humanity.

Frequently Asked Questions

  • How does AI spot and understand human feelings?

    AI figures out emotions by checking facial features, body signals, and text tone. It uses machine learning to find emotion patterns.

  • Can AI's learned empathy be better than human empathy?

    AI can imitate some ways of empathy. However, true empathy comes from deep human emotions that machines cannot feel.

  • Which fields gain the most from empathetic AI?

    Key areas include customer service, healthcare, education, and marketing. Empathetic AI makes human interactions better in these areas.

  • Are there dangers when AI mimics empathy?

    Dangers include fears about privacy, worries about bias, and the ethics of AI affecting emotions.

  • How can creators make sure AI is ethically empathetic?

    To build ethical AI, they need to follow strict rules on data privacy, be transparent, and check for bias. This ensures AI meets our society’s ethical standards.

Streamlining Automated Testing with Github Actions

Streamlining Automated Testing with Github Actions

Automated testing plays a big role in software development today. GitHub Actions is a useful tool for continuous integration (CI). When developers use GitHub Actions for automated testing, it makes their testing processes easier. This leads to better code quality and helps speed up deployment.

Key Highlights

  • Learn how to automate your testing processes with GitHub Actions. This will make your software development quicker and better.
  • We will help you set up your first workflow. You will also learn key ideas and how to use advanced features.
  • This complete guide is great for beginners and for people who want to enhance their test automation with GitHub Actions.
  • You can see practical examples, get help with issues, and find the best ways to work. This will help you improve your testing workflow.
  • Discover how simple it is to connect with test management tools. This can really boost your team’s testing and reporting skills.

Understanding GitHub Actions and Automated Testing

In software development, testing is very important. Test automation helps developers test their code fast and accurately. When you use test automation with good CI/CD tools, like GitHub Actions, it improves the development process a lot.
GitHub Actions helps teams work automatically. This includes test automation. You can begin automated tests when certain events happen. For example, tests can run when someone pushes code or makes a pull request. This ensures that every change is checked carefully.

The Importance of Automation in Software Development

Software development should happen quickly. This is why automation is so important. Testing everything by hand each time there is a change takes a long time. It can also lead to mistakes.
Test automation solves this issue by running test cases without help. This allows developers to focus on other important tasks. They can spend time adding new features or fixing bugs.
GitHub Actions is a powerful tool. It helps you to automate your testing processes. It works nicely with your GitHub repository. You can run automated tests each time you push changes to the code.

Overview of GitHub Actions as a CI/CD Tool

GitHub Actions is a strong tool for CI and CD. It connects well with GitHub. You can design custom workflows. These workflows are groups of steps that happen automatically when certain events take place.
In continuous integration, GitHub Actions is very helpful for improving test execution. It allows you to automate the steps of building, testing, and deploying your projects. When you make a change in the code and push it to your new repository’s main branch, it can kick off a workflow that will, by default, run tests, including any related to Pull Requests (PR), build your application, and deploy it either to a staging area or to production.
This automation makes sure your code is always checked and added. It helps to lower the chances of problems. This also makes the development process easier.

Preparing for Automated Testing with GitHub Actions

Before you start making your automated testing workflow, let’s make sure you have everything ready. This will help your setup run smoothly and be successful.
You need a GitHub account. You also need a repository for your code. It helps to know some basic Git commands while you go through this process.

What You Need to Get Started: Accounts and Tools

If you don’t have a repository, start by making a new one in your GitHub account. This repository will be the main place for your code, tests, and workflow setups.
Next, choose a test automation framework that suits your project’s technology. Some popular choices are Jest for JavaScript, pytest for Python, and JUnit for Java. Each option has a unique way of writing tests.
Make sure your project has the right dependencies. If you use npm as your package manager, run npm ci. This command will install all the necessary packages from your package.json file.

Configuring Your GitHub Repository for Actions

With your repository ready, click on the “Actions” tab. Here, you can manage and set up your workflows. You will organize the automated tasks right here.
GitHub Actions searches for files that organize workflows in your repository. You can locate these files in the .github/workflows directory. They use YAML format. This format explains how to carry out the steps and gives instructions for your automated tasks.
When you create a new YAML file in this directory, you add a new workflow to your repository. This workflow begins when certain events happen. These events might be code pushes or pull requests.

Creating Workflow on GitHub Actions

Pre-Requisites:

  • Push the “Postman” collection and “Environment” file in repository.
  • Install “Newman” in your system.

Create a new workflow:

  • Open your GitHub repository.
  • Click on the “Actions” tab on the top.
  • Click on “New workflow” in the actions page.
  • Click on “Configure” button within “Simple Workflow” in “New workflow” page.
  • You can navigate to the “.github/workflow” directory , where we can configure the default “blank.yml” file.
  • Based on the requirements we can configure the “.yml” file, for example if you want to triggers a particular branch whenever the deployment is done, we need to “configure” the branch name in the “.yml” file.
  • We can configure the workflow to be triggered based on specific events, such as whenever a push or pull request occurs in the specified branch.
  • ALTTEXT

  • Add steps to install NodeJS and Newman in the .yml file
  • ALTTEXT

  • If you want to run the particular collection in your branch, configure the “.yml” file using the below command:
  • ALTTEXT

  • To generate an HTML report, you must include steps to install the htmlextra dependency and establishing a folder to store the report.

The screenshot below demonstrates creating a folder to save the report:

ALTTEXT

The screenshot below illustrates copying the generated HTML report:

ALTTEXT

  • Once the configuration setup is completed click on “Commit changes”
  • ALTTEXT

  • Create a new branch and raise an “PR” to the appropriate branch where you want the workflow.
  • Accept the “PR” from the respective branch.
  • After the “Workflow” is added (or) merged in the respective branch, it will auto trigger the configured file (or) folder every time whenever the deployment is done.

Report Verification:

  • Once the execution is completed, we can see the report in the “Actions” tab.
  • The recent executions are displayed at the top (or) the recent workflows are displayed in the left side of the “Actions” panel.
  • Click on the “Workflow”.
  • Click on “build” where we can see the entire test report.
  • The “html” report is generated under “Artifacts” at the bottom of the workflow run.
  • ALTTEXT

  • When you click on the report, it will be getting download in your local system as a zip file.

Issues Faced:

  • Sometimes the htmlextra report will not be generated if any of the previous steps or any of the tests getting failed in your postman collection, to handle this error we need to handle the issue.
  • To fix the issue we need to handle it with the “if” condition.

ALTTEXT

Enhancing Your Workflow with Advanced Features

Now that you have a simple testing workflow set up, let’s look at how we can make it better. We can improve it by using advanced features from GitHub Actions.
These features let you run tests at the same time. They also help speed up build times. This can make your CI/CD pipeline easier and faster.

Incorporating Parallel Testing for Efficiency

As your test suite gets bigger, it takes more time to run UI tests. GitHub Actions can help make this easier. It allows you to run your new configuration tests in parallel, which is a great way to cut down the time needed for your tests. By breaking your test suite into smaller parts, you can use several runners to run these parts simultaneously and you can even use a test automation tool to subscribe to notifications about the test run ID and the progress.
This helps you receive feedback more quickly. You don’t need to wait for all the tests to end. You can gain insights into certain parts fast.

Here are some ways to use parallel testing:

  • Split by Test Files: Divide your test suite into several files. You can set up GitHub Actions to run them all together.
  • Split by Test Types: If you group your tests by type, like unit, integration, or end-to-end, run each group together.
  • Use a Test Runner with Parallel Support: Some test runners can run tests at the same time. This makes it easier to set up.

Utilizing Cache to Speed Up Builds

Caching is important in GitHub Actions. It helps speed up your build processes. When you save dependencies, build artifacts, or other files that you use often, it can save you time. You won’t have to download or create them again.
Here are some tips for using caching:

  • Find Cachable Dependencies: Look for dependencies that do not change. You can store them in cache. This means you will not need to download them again.
  • Use Actions That Cache Automatically: Some actions, like actions/setup-node, have built-in caching features. This makes things easier.
  • Handle Cache Well: Make sure to clear your cache regularly. This helps you save space and avoid problems from old files.

Monitoring and Managing Your Automated Tests

It is important to keep an eye on the health and success of automated tests. This is as important as creating them. When you understand the results of the workflow, you can repair any tests that fail. This practice helps to keep a strong CI pipeline.
By paying close attention and taking good care of things, you can make sure your tests give the right results. This helps find and fix any problems quickly.

Understanding Workflow Results and Logs

GitHub Actions helps you see each workflow run in a simple way. It shows you the status of every job and step in that workflow. You can easily find this information in the “Actions” tab of your repository.
When you click on a specific workflow run, you can see logs for each job and step. The logs show the commands that were used, the results they produced, and any error messages. This information is helpful if you need to solve problems.
You might want to connect to a test management tool. These tools can help you better report and analyze data. They can show trends in test results and keep track of test coverage. They can also create detailed reports. This makes your test management much simpler.

Debugging Failing Tests and Common Issues

Failing tests are common. They help you see where your code can get better. It is really important to fix these failures well.
Check the logs from GitHub Actions. Focus on the error messages and stack traces. They often provide helpful clues about what caused the issue.
Here is a table that lists some common problems and how to fix them:

Issue Troubleshooting Steps
Test environment misconfiguration Verify environment variables, dependencies, and service configurations
Flakiness in tests Identify non-deterministic behavior, isolate dependencies, and implement retries or mocking
Incorrect assertions or test data Review test logic, data inputs, and expected outcomes

Conclusion

In conclusion, using automated testing with GitHub Actions greatly enhances your software development process by improving speed, reliability, and efficiency. Embracing automation allows teams to streamline repetitive tasks and focus on innovation. Tools like parallel testing further optimize workflows, ensuring code consistency. Regularly monitoring your tests will continuously improve quality. If you require similar automation testing services to boost your development cycle, reach out to Codoid for expert solutions tailored to your needs. Codoid can help you implement cutting-edge testing frameworks and automation strategies to enhance your software’s performance.

Frequently Asked Questions

  • How Do I Troubleshoot Failed GitHub Actions Tests?

    To fix issues with failed GitHub Actions tests, look at the logs for every step of the job that failed. Focus on the error messages, stack traces, and console output. This will help you find the main problem in your code or setup.

Beginner’s Guide: Mastering AI Code Review with Cursor AI

Beginner’s Guide: Mastering AI Code Review with Cursor AI

The coding world understands artificial intelligence. A big way AI helps is in code review. Cursor AI is the best way for developers to get help, no matter how skilled they are. It is not just another tool; it acts like a smart partner who can “chat” about your project well. This includes knowing the little details in each line of code. Because of this, code review becomes faster and better.

Key Highlights

  • Cursor AI is a code editor that uses AI. It learns about your project, coding style, and best practices of your team.
  • It has features like AI code completion, natural language editing, error detection, and understanding your codebase.
  • Cursor AI works with many programming languages and fits well with VS Code, giving you an easy experience.
  • It keeps your data safe with privacy mode, so your code remains on your machine.
  • Whether you are an expert coder or just getting started, Cursor AI can make coding easier and boost your skills.

Understanding AI Code Review with Cursor AI

Cursor AI helps make code reviews simple. Code reviews used to require careful checks by others, but now AI does this quickly. It examines your code and finds errors or weak points. It also suggests improvements for better writing. Plus, it understands your project’s background well. That is why an AI review with Cursor AI is a vital part of the development process today.

With Cursor AI, you get more than feedback. You get smart suggestions that are designed for your specific codebase. It’s like having a skilled developer with you, helping you find ways to improve. You can write cleaner and more efficient code.

Preparing for Your First AI-Powered Code Review

Integrating Cursor AI into your coding process is simple. It fits well with your current setup. You can get help from AI without changing your usual routine. Before starting your first AI code review, make sure you know the basics of the programming language you are using.

Take a bit of time to understand the Cursor AI interface and its features. Although Cursor is easy to use, learning what it can do will help you get the most from it. This knowledge will make your first AI-powered code review a success.

Essential tools and resources to get started

Before you begin using Cursor AI for code review, be sure to set up a few things:

  • Cursor AI: Get and install the newest version of Cursor AI. It runs on Windows, macOS, and Linux.
  • Visual Studio Code: Because Cursor AI is linked to VS Code, learning how to use its features will help you a lot.
  • (Optional) GitHub Copilot: You don’t have to use GitHub Copilot, but it can make your coding experience better when paired with Cursor AI’s review tools.

Remember, one good thing about Cursor AI is that it doesn’t require a complicated setup or API keys. You just need to install it, and then you can start using it right away.
It’s helpful to keep documentation handy. The Cursor AI website and support resources are great when you want detailed information about specific features or functions.

Setting up Cursor AI for optimal performance

To get the best out of Cursor AI, spend some time setting it up. First, check out the different AI models you can use to help you understand coding syntax. Depending on your project’s complexity and whether you need speed or accuracy, you can pick from models like GPT-4, Claude, or Cursor AI’s custom models.

If privacy matters to you, please turn on Privacy Mode. This will keep your code on your machine. It won’t be shared during the AI review. This feature is essential for developers handling sensitive or private code.

Lastly, make sure to place your project’s rules and settings in the “Rules for AI” section. This allows Cursor AI to understand your project and match your coding style. By doing this, the code reviews will be more precise and useful.

Step-by-Step Guide to Conducting Your First Code Review with Cursor AI

Conducting an AI review with Cursor AI is simple and straightforward. It follows a clear step-by-step guide. This guide will help you begin your journey into the future of code review. It explains everything from setting up your development space to using AI suggestions.

This guide will help you pick the right code for review. It will teach you how to run an AI analysis and read the results from Cursor AI. You will also learn how to give custom instructions to adjust the review. Get ready to find a better and smarter way to improve your code quality. This guide will help you make your development process more efficient.

Step 1: Integrating Cursor AI into Your Development Environment

The first step is to ensure Cursor AI works well in your development setup. Download the version that matches your operating system, whether it’s Windows, macOS, or Linux. Then, simply follow the simple installation steps. The main advantage of Cursor AI is that it sets up quickly for you.

If you already use VS Code, you are in a great spot! Cursor AI works like VS Code, so it will feel similar in terms of functionality. Your VS Code extensions, settings, and shortcuts will work well in Cursor AI. When you use privacy mode, none of your code will be stored by us. You don’t have to worry about learning a new system.

This easy setup helps you begin coding right away with no extra steps. Cursor AI works well with your workflow. It enhances your work using AI, and it doesn’t bog you down.

Step 2: Selecting the Code for Review

With Cursor AI, you can pick out specific code snippets, files, or even whole project folders to review. You aren’t stuck to just looking at single files or recent changes. Cursor AI lets you explore any part of your codebase, giving you a complete view of your project.

Cursor AI has a user-friendly interface that makes it easy to choose what you want. You can explore files, search for code parts, or use git integration to check past commits. This flexibility lets you do focused code reviews that meet your needs.

Cursor AI can understand what your code means. It looks at the entire project, not just the part you pick. This wide view helps the AI give you helpful and correct advice because it considers all the details of your codebase.

Step 3: Running the AI Review and Interpreting Results

Once you choose the code, it is simple to start the AI review. Just click a button. Cursor AI will quickly examine your code. A few moments later, you will receive clear and easy feedback. You won’t need to wait for your co-workers anymore. With Cursor AI, you get fast insights to improve your code quality.

Cursor AI is not just about pointing out errors. It shows you why it gives its advice. Each piece of advice has a clear reason, helping you understand why things are suggested. This way, you can better learn best practices and avoid common mistakes.

The AI review process is a great chance to learn. Cursor AI shows you specific individual review items that need fixing. It also helps you understand your coding mistakes better. This is true whether you are an expert coder or just starting out. Feedback from Cursor AI aims to enhance your skills and deepen your understanding of coding.

Step 4: Implementing AI Suggestions and Finalizing Changes

Cursor AI is special because it works great with your tasks, especially in the terminal. It does more than just show you a list of changes. It offers useful tips that are easy to use. You won’t need to copy and paste code snippets anymore. Cursor AI makes everything simpler.

The best part about Cursor AI is that you are in control. It offers smart suggestions, but you decide what to accept, change, or ignore. This way of working means you are not just following orders. You are making good choices about your code.

After you check and use the AI tips, making your changes is simple. You just save your code as you normally do. This final step wraps up the AI code review process. It helps you end up with cleaner, improved, and error-free code.

Best Practices for Leveraging AI in Code Reviews

To make the best use of AI in code reviews, follow good practices that can improve its performance. When you use Cursor AI, remember it’s there to assist you, not to replace you.
Always check the AI suggestions carefully. Make sure they match what your project needs. Don’t accept every suggestion without understanding it. By being part of the AI review, you can improve your code quality and learn about best practices.

Tips for effective collaboration with AI tools

Successful teamwork with AI tools like Cursor AI is very important because it is a team effort. AI can provide useful insights, but your judgment matters a lot. You can change or update the suggestions based on your knowledge of the project.

Use Cursor AI to help you work faster, not control you. You can explore various code options, test new features, and learn from the feedback it provides. By continuing to learn, you use AI tools to improve both your code and your skills as a developer.

Clear communication is important when working with AI. It is good to say what you want to achieve and what you expect from Cursor AI. Use simple comments and keep your code organized. The clearer your instructions are, the better the AI can understand you and offer help.

Common pitfalls to avoid in AI-assisted code reviews

AI-assisted code reviews have several benefits. However, you need to be careful about a few issues. A major problem is depending too much on AI advice. This might lead to code that is correct in a technical sense, but it may not be creative or match your intended design.

AI tools focus on patterns and data. They might not fully grasp the specific needs of your project or any design decisions that are different from usual patterns. If you take every suggestion without thinking, you may end up with code that works but does not match your vision.

To avoid problems, treat AI suggestions as a starting point rather than the final answer. Review each suggestion closely. Consider how it will impact your codebase. Don’t hesitate to reject or modify a suggestion to fit your needs and objectives for your project.

Conclusion

In conclusion, getting good at code review with Cursor AI can help beginners work better and faster. Using AI in the code review process improves teamwork and helps you avoid common mistakes. By adding Cursor AI to your development toolset and learning from its suggestions, you can make your code review process easier. Using AI in code reviews makes your work more efficient and leads to higher code quality. Start your journey to mastering AI code review with Cursor AI today!

For more information, subscribe to our newsletter and stay updated with the latest tips, tools, and insights on AI-driven development!

Frequently Asked Questions

  • How does Cursor AI differ from traditional code review tools?

    Cursor AI is not like regular tools that just check grammar and style. It uses AI to understand the codebase better. It can spot possible bugs and give smart suggestions based on the context.

  • Can beginners use Cursor AI effectively for code reviews?

    Cursor AI is designed for everyone, regardless of their skill level. It has a simple design that is easy for anyone to use. Even beginners will have no trouble understanding it. The tool gives clear feedback in plain English. This makes it easier for you to follow the suggestions during a code review effectively.

  • What types of programming languages does Cursor AI support?

    Cursor AI works nicely with several programming languages. This includes Python, Javascript, and CSS. It also helps with documentation formats like HTML.

  • How can I troubleshoot issues with Cursor AI during a code review?

    For help with any problems, visit the Cursor AI website. They have detailed documentation. It includes guides and solutions for common issues that happen during code reviews.

  • Are there any costs associated with using Cursor AI for code reviews?

    Cursor AI offers several pricing options. They have a free plan that allows access to basic features. This means everyone can use AI for code review. To see more details about their Pro and Business plans, you can visit their website.

Unleashing the Power of Generative AI in eLearning

Unleashing the Power of Generative AI in eLearning

Generative AI is quickly changing the way we create and enjoy eLearning. It brings a fresh approach to personalized and engaging elearning content, resulting in a more active and effective learning experience. Generative AI can analyze data to create custom content and provide instant feedback, allowing for enhanced learning processes with agility. Because of this, it is set to transform the future of digital education.

Key Highlights

  • Generative AI is transforming eLearning by personalizing content and automating tasks like creating quizzes and translations.
  • AI-powered tools analyze learner data to tailor learning paths and offer real-time feedback for improvement.
  • Despite the benefits, challenges remain, including data privacy concerns and the potential for bias in AI-generated content.
  • Educators must adapt to integrate these new technologies effectively, focusing on a balanced approach that combines AI with human instruction.
  • The future of learning lies in harnessing the power of AI while preserving the human touch for a more engaging and inclusive educational experience.
  • Generative AI can create different content types, including text, code, images, and audio, making it highly versatile for various learning materials.

The Rise of GenAI in eLearning

The eLearning industry is always changing. It adapts to what modern learners need. Recently, artificial intelligence, especially generative AI, has become very important. This strong technology does more than just automate tasks. It can create, innovate, and make learning personal, starting a new era for education.
Generative AI can make realistic simulations and interactive content. It can also tailor learning paths based on how someone is doing. This change is moving us from passive learning to a more engaging and personal experience. Both educators and learners can benefit from this shift.

Defining Generative AI and Its Relevance to eLearning

At its core, generative AI means AI tools that can create new things like text, images, audio, or code. Unlike regular AI systems that just look at existing data, generative AI goes further. It uses this data to make fresh and relevant content.
This ability to create content is very important for eLearning. Making effective learning materials takes a lot of time. Now, AI tools can help with this. They allow teachers to spend more time on other important tasks, like building the curriculum and interacting with students.
Generative AI can also look at learner data. It uses this information to create personalized content and learning paths. This way, it meets the unique needs of each learner. As a result, the learning experience can be more engaging and effective.

Historical Evolution and Current Trends

The use of artificial intelligence in the elearning field is not brand new. In the beginning, it mostly helped with simple tasks, like grading quizzes and giving basic feedback. Now, with better algorithms and machine learning, we have generative AI, which is a big improvement.
Today, generative AI does much more than just automate tasks. It builds interactive simulations, creates personalized learning paths, and adjusts content to fit different learning styles. This change to a more flexible, learner-focused approach starts a new chapter in digital learning.
Right now, there is a trend that shows more and more use of generative AI to solve problems like accessibility, personalization, and engagement in online learning. As these technologies keep developing, we can look forward to even more creative uses in the future.

Breakthroughs in Content Development with GenAI

Content development in eLearning has been a tough task that takes a lot of time and effort. Generative AI is changing this with tools that make development faster and easier.
Now, you can create exciting course materials, fun quizzes, and realistic simulations with just a few clicks. Generative AI is helping teachers create engaging learning experiences quickly and effectively.

Automating Course Material Creation

One major advancement of generative AI in eLearning is that it can create course materials automatically. Tasks that used to take many days now take much less time. This helps in quickly developing and sharing training materials. Here’s how generative AI is changing content development:

  • Text Generation: AI can produce good quality written content. This includes things like lecture notes, summaries, and complete study guides.
  • Multimedia Creation: For effective learning, attractive visuals and interactive elements are important. AI tools can make images, videos, and interactive simulations, making learning better.
  • Assessment Generation: There’s no need to make quizzes and tests by hand anymore. AI can automatically create assessments that match the learning goals, ensuring a thorough evaluation.

This automation gives educators and subject matter experts more time. They can focus on teaching methods and creating the curriculum. This leads to a better learning experience.

Enhancing Content Personalization for Learners

Generative AI does more than just create content. It helps teachers make learning more personal by using individual learner data. By looking at how students progress, their strengths, and what they need to work on, AI can customize learning paths and give tailored feedback.
Adaptive learning is a way that changes based on how well a learner is doing. With generative AI, it gets even better. As the AI learns more about a student’s habits, it can adjust quiz difficulty, suggest helpful extra materials, or recommend new learning paths. This personal touch keeps students engaged and excited.
In the end, generative AI helps make education more focused on the learner. It meets each person’s needs and promotes a better understanding of the subject. Moving away from a one-size-fits-all method to personalized learning can greatly boost learner success and knowledge retention.

Impact of GenAI on Learning Experience

Generative AI is changing eLearning in many ways. It goes beyond just creating content and personalizing lessons. It is changing how students experience education. The old online learning method was often boring and passive. Now, it is becoming more interactive and fun. Learning is adapting to fit each student’s needs.
This positive change makes learning more enjoyable and effective. It helps students remember what they learn and fosters a love for education.

Customized Learning Paths and Their Advantages

Imagine a learning environment that fits your style and speed. It gives you personalized content and challenges that match your strengths and weaknesses. Generative AI makes this happen by creating custom learning paths. This is a big change from the usual one-size-fits-all learning approach.
AI looks at learner data like quiz scores, learning styles, and time spent on different modules. With this, AI can analyze a learner’s performance and create unique learning experiences for each learner. Instead of just moving through a course step by step, you can spend more time on the areas you need help with and move quickly through things you already understand.
This kind of personalization, along with adding interactive elements and getting instant feedback, leads to higher learner engagement. It also creates more effective learning experiences for you.

Real-time Feedback and Adaptive Learning Strategies

The ability to get real-time, helpful feedback is very important for effective learning. Generative AI tools are great at this. They give learners quick insights into how they are doing and help them improve.
AI doesn’t just give right-or-wrong answers. Its algorithms can look at learner answers closely. This way, they can provide detailed explanations, find common misunderstandings, and suggest helpful resources for further learning, such as Google Translate for language assistance. For example, if a student has trouble with a specific topic, the AI can change the difficulty level. It might recommend extra practice tasks or even a meeting with an instructor.
This ongoing feedback and the chance to change learning methods based on what learners need in real-time are key to building a good learning environment.

Challenges and Solutions in Integrating GenAI

The benefits of generative AI in eLearning can be huge. But there are also some challenges that content creators must deal with to use it responsibly and well. Issues like data privacy, possible biases in AI algorithms, and the need to improve skills for educators are a few of the problems we need to think about carefully.
Still, if we recognize these challenges and find real solutions, we can use generative AI to create a better learning experience. This can lead to a more inclusive, engaging, and personalized way of learning for everyone.

Addressing Data Privacy Concerns

Data privacy is very important when using generative AI in eLearning. It is crucial to handle learner data carefully. This data includes things like how well students perform, their learning styles, and their personal preferences.
Schools and developers should focus on securing the data. This includes using data encryption and secure storage. They should also get clear permission from learners or their parents about how data will be collected and used. Being open about these practices helps build trust and ensures that data is managed ethically.
It is also necessary to follow industry standards and rules, like GDPR and FERPA. This helps protect learner data and ensures that we stay within legal guidelines. By putting data privacy first, we can create a safe learning environment. This way, learners can feel secure sharing their information.

Overcoming Technical Barriers for Educators

Integrating generative AI into eLearning is not just about using new tools. It also involves changing how teachers think and what skills they have. To help teachers, especially those who do not know much about AI, we need to offer good training and support.
Instructional designers and subject matter experts should learn how AI tools function, what they can and cannot do, and how to effectively use them in their teaching. Offering training in AI knowledge, data analysis, and personal learning methods is very important.
In addition, making user-friendly systems and providing ongoing support can help teachers adjust to these new tools. This will inspire them to take full advantage of what AI can offer.

Testing GenAI Applications

Testing is very important before using generative AI in real-world learning settings.
Careful testing
makes sure these AI tools are accurate, reliable, and fair. It also helps find and fix possible biases or problems.
Testing should include different people. This means educators, subject matter experts, and learners should give their input. Their feedback is key to checking how well the AI applications work. We need to keep testing, improving, and assessing the tools. This is vital for building strong and dependable AI tools that improve the learning experience.

Conclusion

GenAI is changing the eLearning industry. It helps make content creation easier and personalizes learning experiences. This technology can provide tailored learning paths and real-time adjustment strategies. These features improve the overall education process.
Still, using GenAI comes with issues. There are concerns about data privacy and some technical challenges. Yet, if we find the right solutions, teachers can use its benefits well.
The future of eLearning depends on combining human skills with GenAI innovations. This will create a more engaging and effective learning environment. Keep an eye out for updates on how GenAI will shape the future of learning.

Frequently Asked Questions

  • How does GenAI transform traditional eLearning methods?

    GenAI changes traditional elearning. It steps away from fixed content and brings flexibility. It uses AI to create different content types that suit specific learning goals. This makes the learning experience more dynamic and personal.

  • Can GenAI replace human instructors in the eLearning industry?

    GenAI improves the educational experience by adapting to various learning styles and handling tasks automatically. However, it will not take the place of human teachers. Instead, it helps teachers by allowing them to concentrate on mentoring students and on more advanced teaching duties.

  • What are the ethical considerations of using GenAI in eLearning?

    Ethical concerns with using GenAI in elearning are important. It's necessary to protect data privacy. We must also look at possible bias in the algorithms. Keeping transparency is key to keeping learner engagement and trust. This should all comply with industry standards.

Comprehensive LLM Software Testing Guide

Comprehensive LLM Software Testing Guide

Large Language Model (LLM) software testing requires a different approach compared to conventional mobile, web, and API testing. This is due to the fact that the output of such LLM or AI applications is unpredictable. A simple example is that even if you give the same prompt twice, you will receive unique outputs from the LLM model. We faced similar challenges when we ventured into GenAI development. So based on our experience of testing the AI applications we developed and other LLM testing projects we have worked on, we were able to develop a strategy for testing AI and LLM solutions. So in this blog, we will be helping you get a comprehensive understanding of LLM software testing.

LLM Software Testing Approach

By identifying the quality problems associated with LLMs, you can effectively strategize your LLM software testing approach. So let’s start by comprehending the prevalent LLM quality and safety concerns and learn how to find them with LLM quality checks.

Hallucination

As the word suggests, Hallucination is when your LLM application starts providing irrelevant or nonsensical responses. It is in reference to how humans hallucinate and see things that do not exist in real life and think them to be real.

Example:

Prompt: How many people are living on the planet Mars?

Response: 50 million people are living on Mars.

How to Detect Hallucinations?

Given that the LLM can hallucinate in multiple ways for different prompts, detecting these hallucinations is a huge challenge that we have to overcome during LLM software testing. We recommend using the following methods,

Check Prompt-Response Relevance – Checking the relevance between a given prompt and response can assist in recognizing hallucinations. We can use the BLEU scoreBLEU scoreMeasures how closely a generated text matches reference texts by comparing short sequences of words and BERT scoreBERT scoreAssesses how similar a generated text is to reference texts by comparing their meanings using BERT language model embeddings to check the relevance between prompt and LLM response.

  • BLEU score is calculated with exact matching by utilizing the Python Evaluate library. The score ranges from 0 to 1 and a higher score indicates a greater similarity between your prompt and response.
  • BERT score is calculated with semantic matching and it is a powerful evaluation metric to measure text similarity.

Check Against Multiple Responses – We can check the accuracy of the actual response by comparing it to various randomly generated responses for a given prompt. We can use Sentence Embedding Cosine Distance & LLM Self-evaluation to check the similarity.

Testing Approach

  1. Shift Left Testing – Before deploying your LLM application, evaluate your model or RAG implementation thoroughly
  2. Shift Right Testing – Check BERT score for production prompts and responses

Prompt Injections

Jailbreak – Jailbreak is a direct prompt injection method to get your LLM to ignore the established safeguards that tell the system what not to do. Let’s say a malicious user asks a restricted question in the Base64 formatBase64 formatIt is a way of encoding binary data into a text format using a set of 64 different ASCII characters , your LLM application should not answer the question. Security experts have already identified various Jailbreaking methods in commonly used LLMs. So it is important to analyze such methods and ensure your LLM system is not affected by them.

Indirect Injection

  • Hidden prompts are often added by attackers in your original prompt.
  • Attackers intentionally make the model to get data from unreliable sources. Once training data is incorrect, the response from LLM will also be incorrect.

Refusals – Let’s say your LLM model refuses to answer for a valid prompt, it could be because the prompt might be modified before sending it to LLM.

How to prevent Prompt Injection?

  • Ensure your training data doesn’t have sensitive information
  • Ensure your model doesn’t get data from unreliable external sources
  • Perform all the security checks for LLM APIs
  • Check substrings like (Sorry, I can’t, I am not allowed) in response to detect refusals
  • Check response sentiment to detect refusals

RAG Injection

RAG is an AI framework that can effectively retrieve and incorporate outside information with the prompt provided to LLM. This allows the model to generate an accurate response when contextual cues are given by the user. The outside or external information is usually retrieved and stored in a vector database.

If poisoned data is obtained from an external source, how will LLM respond? Clearly, your model will start producing hallucinated responses. This phenomenon in LLM software testing is referred to as RAG injection.

Data Leakage

Data Leakage occurs when confidential or personal information is exposed either through a Prompt or LLM response.

Data Leak from Prompt – Let’s assume a user mentions their credit card number or password in their prompt. In that case, the LLM application must identify this information to be confidential even before it sends the request to the model for processing.

Data Leak from Response – Let’s take a Healthcare LLM application as an example here. Even if a user asks for medical records, the model should never disclose sensitive patient information or personal data. The same applies to other types of LLM applications as well.

How to prevent Data Leakage?

  • Ensure training data doesn’t store any personal or confidential information.
  • Use Regex to check all the incoming prompts or outgoing responses for Personal Identifiable Information.(PII)

Grounding Issues

Grounding is a method for tailoring your LLM to a particular domain, persona, or use case. We can cover this in our LLM software testing approach through prompt instructions. When an LLM is limited to a specific domain, all of its responses must fall within that domain. So manual testers have a vital responsibility here in identifying any LLM grounding problems.

Testing Approach

  • Ask multiple questions that are not relevant to the Grounding instructions.
  • Add an active response monitoring mechanism in Production to check the Groundedness score.

Token Usage

There are numerous LLM APIs in the market that charge a fee for the tokens generated from the prompts. Let’s say your LLM application is generating more tokens after a new deployment, this will result in a surge in the monthly billing for API usage.

The pricing of LLM products for many companies is typically determined by Token consumption and other resources utilized. If you don’t calculate & monitor token usage, your LLM product will not make the expected revenue from it.

Testing Approach

  • Monitor token usage and the monthly cost constantly.
  • Ensure the response limit is working as expected before each deployment.
  • Always look for optimizing token usage.

General LLM Software Testing Tips

For effective LLM software testing, there are several key steps that should be followed. The first step is to clearly define the objectives and requirements of your application. This will provide a clear roadmap for testing and help determine what aspects need to be focused on during the testing process

Moreover, continuous integration (CI) plays an important role in ensuring a smooth development workflow by constantly integrating new code into the existing codebase while running automated tests simultaneously. This helps catch any issues early on before they pile up into bigger problems.

It is crucial to have a dedicated team responsible for monitoring and managing quality assurance throughout the entire development cycle. A competent team will ensure effective communication between developers and testers resulting in timely identification and resolution of any issues found during testing.

Conclusion:

LLM software testing may seem like a daunting and time-consuming process, but it is an essential step in delivering a high-quality product to end-users. By following the steps outlined above, you can ensure that your LLM application is thoroughly tested and ready for success in the market. As it is an evolving technology, there will be rapid advancements in the way we approach LLM application testing. So make sure to keep updating your approach by keeping yourself updated. Also, make sure to keep an eye out on this space for more informative content.

How to do Mobile App Testing? A Complete Guide

How to do Mobile App Testing? A Complete Guide

You might have the best app idea on paper, but in order to be successful in this highly competitive industry, you need a Grade-A QA company such as us on your team. Mobile app testing companies have a huge role to play in an app’s success as mobile app testing is not just about performing UI tests. It is about ensuring that you achieve 360-degree coverage with well-planned-out test cases. Being an experienced mobile app testing company, we understand the factors that impact the real-world performance of your app. So using our expertise, we have prepared this conclusive guide that includes a mobile app testing checklist, a list of the best mobile app testing tools, and so on.

Mobile App Testing Checklist

In general, using checklists is a great way to keep all your content organized. Likewise, mobile app testers can make use of this checklist to keep track of the level of progress and ensure that all the important tests are performed without fail. It can also be very useful in preventing testers from repeating tests that they have already done.

App Interruption

Smartphones of today are designed to help their users multitask with ease. In a fast-paced world, the end-user will not be happy with your application if it doesn’t handle the various interruptions that it might face during everyday usage. So you can add the below-listed app interruption test scenarios to your test suite to avoid bad reviews & eventual app uninstallation.

1. Switch to a different app and come back again to your app screen to see how it reacts.

2. Answer or reject an incoming call when you are using your app to ensure that the app doesn’t crash or restart.

3. Click on another app’s push notification and cause an interruption while you are using your app to verify if the app returns to the same state and screen when you left it.

4. If your mobile app requires to be connected to a Bluetooth device or any other hardware, then remove the device while it is being used and see how your app handles the interruption.

5. Abruptly close the app when you are in the middle of a purchase transaction and reopen it. The app should show whether the transaction was a success or a failure.

6. Set an alarm to ring while your app is being used to ensure the app’s screen does not hang or freeze.

7. Play a video or bring your app to the foreground and leave the device unattended until it auto locks or goes to sleep. Unlock the device and check the app is where you have left.

8. Use your app when the battery is nearing the low battery threshold. You will be able to ensure that the mobile app does not crash or freeze once you have taken action on the low battery alert.

9. Test how your app reacts while downloading a large file in the background.

10. Update the app to the new version by clicking on the update available alert to see if it crashes.

11. Your app shouldn’t crash or freeze when it tries to access a corrupted file from the storage.

Testing under different Network Conditions

Did you know that 20% of mobile apps crash due to network issues? The root cause of such an issue is testing the mobile app under optimal conditions and not considering the real-world conditions. In order to ensure your mobile app’s stability, you need to test how your app adapts to the different network conditions.

1. Switch to Airplane mode while uploading a file or when your app screen is loading.

2. Change the network from 4G to 3G and vice versa to check how your app handles the change in the network.

3. Change the network when a video is being played.

4. Switch from WiFi to Mobile Data and vice versa to see how the app is impacted.

5. Change the Data connection between SIMs(SIM 1 to SIM2 and vice versa) while using your app.

6. Turn off your Data connection or WiFi while using your app to ensure it doesn’t crash or freeze as the offline functionalities shouldn’t be affected.

7. Check your app behavior when it enters or exits a Dead zone. Dead zones are usually areas where mobile phone services are not available. (Scenario for Real device testing)

8. Your app might not face any issues while downloading large files when the data connection is strong. But it should also be able to download even when the network isn’t that strong.

Check Battery Usage & Resource Utilisation

No end-user will like an app that drains their battery or uses too much of their device’s resources. So ensuring that your mobile app performs efficiently with limited resources is important. But testing such parameters using emulators and simulators is not possible. Being a top mobile app testing company, we overcome this challenge by using real devices for mobile app testing. We will be exploring the various tools that will be coming in handy for this purpose. But first, let’s take a look at the different test scenarios that will help you identify battery drain and resource management issues.

1. Test the app with low network bandwidth as it will be helpful in inspecting if there is any rapid battery drain.

2. Open and keep your competitor apps active in the background while you open or use other apps to see how the phone shares its resource between all the apps. For example, Online traders are known to use multiple trading apps simultaneously. So if your mobile app is for trading, it should be able to withstand extreme CPU load conditions when other competitor trading apps are active and open.

3. Go to any screen which supports infinite scrolling and scroll for some time to check how the app utilizes the device’s resources.

4. If you’re testing a camera app, then open the other camera apps and keep them running in the background to switch between those apps when the camera is active.

5. Check how much battery, RAM, and data your app (uptime usage) consumes when used as a typical end-user for both the foreground and background scenarios.

6. Try to download files using your mobile app when your phone does not have enough memory to accommodate them.

7. Compare battery usage after removing the cached files.

8. Monitor battery usage when your app calculates the device’s location.

9. Ensure that your app responds to the device’s battery saver mode in a way that avoids using the limited power unnecessarily.

10. If your app prefers to do power-intensive tasks once the phone battery is sufficiently charged, check whether those tasks are invoked when the battery is fully or sufficiently charged.

11. If a mobile app reads data from a sensor even when the app no longer needs the sensor data or when it is in the background, the phone battery will be consumed unnecessarily. So ensure your app does not use sensors when they are not in use.

12. Ensure that any third-party libraries used in your app don’t waste resources or perform any actions more often than needed.

Usability

Sometimes, a mobile app might have all the features an end-user might need. But if the usability of all those very useful features is low, the end-users will have a bad user experience. So let’s explore the various test scenarios that will be instrumental in ensuring that the best user experience is delivered without any exceptions.

1. Autofill is such a useful feature that will positively impact the user experience. So ensure that the users are able to input any forms quickly using the autofill suggestions.

2. Check if the buttons are large enough to click.

3. Provide enough spaces between the menus and buttons to avoid accidental clicks.

4. Test if all the buttons or clickable elements are responsive as the users will be annoyed if nothing happens after clicking a button.

5. Ensure your app does not have deep navigation.

6. If a user has provided any wrong inputs, ensure your app highlights the errors and performs a horizontal shake to alert the user.

7. Ensure the search term is still displayed even after showing the results.

8. The order and position of the buttons and text boxes ensure a seamless flow for the end-user.

9. Check if the menu icons are displayed with labels.

10. Since most users operate their smartphone using just their thumb finger, test your mobile app using only one finger to see how convenient it is.

Content

1. If your app has a news feed or has similar social media functionalities, ensure that the latest content is shown in the feeds and that appropriate alerts are sent to the user when the app remains unused for a while.

2. Make sure to not overstuff the content in both your mobile app screen and in your app’s notifications as readability is very important.

3. Ensure that the font style, color schemes, and styles are uniform across the entire mobile app.

Gestures

1. Ensure that the gestures are easy to reproduce as they shouldn’t confuse your users.

2. Most mobile users are familiar with known gestures like Tap, Double Tap, Swipe, Slide, Drag, Flick, Long press, Pinch, & Rotate. So prefer commonly known and used gestures are instead of custom gestures.

3. Check if your app works smoothly when you press multiple touch points simultaneously.

4. Now that gestures like side screen swipes and bottom screen swipes have been introduced to replace the back, home, and recent apps buttons in Android devices, it is important that your app’s navigation or features are not impacted by it.

For example, if your app has a crop feature, then keeping the crop lines at the corners can trigger the side screen swipe and close the app instead of cropping the image.

Permission

With such a majority of the world’s population using smartphones, the need for privacy and awareness surrounding it has been on the rise. The end-users are ever more concerned about their privacy and data. So as a rule of thumb, make sure you don’t request device permissions that you do not need for your app. Apart from the privacy concerns, the dependencies that are created can also be avoided.

1. If your mobile app requires location data to function, grant the location permission and close the app. Revoke the location permission and relaunch the app to see how it functions.

2. Likewise, revoke the contact access permission and relaunch the app to see how it responds.

3. Clear the app data and check how your mobile app to see its impact.

Performance

Nobody likes to use a slow mobile app that can’t keep pace with the fast-moving world. So any mobile app that doesn’t go through performance testing will most probably get lost in the competition. Here’s the checklist of test cases to follow.

1. If your mobile app uses a contact list, import a huge list and check how your app handles it.

2. Ensure your mobile app does not crash when launched multiple times in a row.

3. Load the server with simulated traffic and perform functional testing to observe how the app works with peak load.

4. Compare screen loading times with an older device.

Accessibility for Mobile Apps

With the exception of a few, almost every adult uses a smartphone. A report from the CDC states that 1 in every 4 adult Americans suffers from some form of disability. Smartphones and mobile apps have become a basic necessity in today’s technological world as it is an integral part of our day-to-day life. So let’s explore the various accessibility-based test scenarios for both Android and iOS mobile apps.

Common to both Android and iOS

1. Ensure the buttons are keyboard operable and that they show keyboard focus visibility.

2. Validate if the buttons convey their accessible name, role, value, and state to TalkBack users.

3. Check if the links are meaningful and have a purpose.

4. Ensure the progress bars conveys their accessible name, role, and current value to TalkBack users.

5. The appearance of progress spinners must be conveyed to TalkBack users either via focus management or screen reader announcements

6. Verify if the SeekBar sliders have TextView labels connected using the android:labelFor.

7. Check whether the Switch controls have the ‘android:text’ labels spoken to TalkBack users.

8. Test to see if the dynamic content changes are notified to Talkback users through spoken accessibility announcements or focus management.

9. Ensure that your user login session does not timeout without providing any accessible timeout warning or a session extension method.

10. Verify whether alerts are conveyed through TalkBack or grab users attention

11. Validate if the Modal dialogs capture the focus of the screen reader and trap the keyboard.

12. Ensure the Accordion components announce their accessible names of both the expanded and collapsed states.

13. Check if the autocomplete suggestions are announced to the users when they appear. Also, validate if they are operable for TalkBack and Keyboard-only users

14. Make sure there is a way to stop the Carousel’s movement.

15. Ensure the current rating value has an accessible name and that the value is conveyed to the end-user as well.

16. Verify if the sorted state of a sortable table column headers is conveyed to the users.

17. Ensure the state of the toggle button is conveyed to the end-user.

18. The label text should be visible to the end-user at all times.

19. Check whether the groups of related inputs convey their group legend text to TalkBack users.

20. Ensure that the native date picker controls are used over plain text inputs or custom date picker controls.

21. Validate if the radio buttons convey their individual label, group legend, and checked state to the end-users.

22. Check if the Select dropdown controls have an associated visible text label.

23. Make sure the form validation error messages are conveyed to the user when the errors are displayed or when a user is focused on invalid inputs.

24. Check if the data tables are in logical reading order and if the data cells are conveying their row and column header text to TalkBack users.

25. Videos should include closed captions for the deaf and ‘hard of hearing’ users.

26. Likewise, the videos must also have audio descriptions or separate audio-described versions for users who have vision disabilities.

27. If you have used any decorative or redundant images, make sure that such images don’t have any alternative text equivalents as they should be hidden from the screen readers.

Exclusive to iOS

1. Check if the click or touch target size is large enough without having to zoom in.

2. Make sure that the touch targets don’t overlap.

3. Even if there are any compact touch targets, ensure they are not placed adjacent to each other.

4. If the active elements have the same target, make sure those elements are grouped into a one-touch target.

5. Ensure that the application doesn’t rely on kinetic motion (detected by the accelerometer) alone.

6. Even if there are any motion-sensitive features, verify if they can be disabled by the users.

7. Check if the application is fully or heavily reliant on voice control alone.

8. Likewise, the mobile app shouldn’t be too reliant on gestures alone too.

9. Check if the movement of the physical device causes any non-essential visual motion or animations like parallax effects.

10. Validate if the users are able to disable any non-essential motion-sensitive visual effects.

11. Make sure that the content doesn’t flash more than thrice every second. It can be allowed if the flashing content is very small or if the contrast of the flashes doesn’t violate the general flash thresholds.

12. Verify if the alternative text is available for custom emojis in custom keyboards.

Mobile App Testing Tools

There are various open-source and paid tools when it comes to mobile app testing. You might come up with some of the mobile app testing strategies, but successful implementation of them depends on the mobile app testing tools that are available and your proficiency in using them. That is why we have a dedicated R&D team that will identify the various new tools and analyze them to see if they can enhance our testing process. Since we have used various mobile app testing tools in our very own projects, we are aware of some of the best tools used by numerous mobile app testing companies all over the world. So let’s take a look at the list of the best mobile app testing tools, starting with Appium.

Appium

Appium is one of the most popular open-source automation tools used by testers. Like how we have Selenium for performing automation tests on Web pages, Appium is instrumental in helping us execute automation tests on different mobile devices. Since most organizations are keen on developing both web-based and mobile-based apps, using Appium for mobile app automation is one of the best practices. So let’s explore the many useful features and benefits of Appium that make it so popular.

Features
  • Appium is an open-source tool that has great community support.
  • It can be used to perform tests on both Android and iOS apps.
  • You can use Appium to test hybrid, native, and web-based applications.
  • Appium’s cross-platform testing capabilities make Android testing on macOS, Windows testing on Linux, and iOS testing on macOS possible.
  • It supports all the frameworks and all the prominent programming languages as well.
  • The Appium server processes requests from the Appium client and forwards them to the real devices where the test scripts are automated.
  • It generally uses JavaScript as the server language to fetch and transfer the data by communicating with the client and the user.
  • There is no need to install any application on the mobile to trigger the testing.

TestComplete

TestComplete is an Automation Testing tool used for testing web apps, native mobile Android & iOS apps, desktop apps, and API services as well. TestComplete supports various frameworks such as Keyword Driven, Data-Driven, and also BDD. It also allows the users to use various programming languages such as JavaScript, Python, VBScript, etc… for scripting. But what sets it apart is that it has a record and playback feature that enables its users to perform automation without having any scripting knowledge. TestComplete uses the GUI Object Recognition Algorithm with AI capabilities to identify the elements across the application and capture them. And If there is any change in the element’s value, it will automatically detect the exact change in the object and list it out under the Test Log.

Numerous mobile app testing companies use TestComplete in their various projects to get in-depth reports that provide the status of all the tests across web, mobile, and desktop apps. It even provides us the entire recording of the test execution that aids in the visual debugging of issues, captures the screenshot of every action performed on the application, and highlights the element on which the action was performed.

Features
  • The Record and Replay feature isn’t limited to just basic actions as it can be used to even automate complex scenarios without us having to write a single line of code.
  • TestComplete is also well-known for its Keyword Driven Testing which enables testers to use drag and drop gestures to write easy-to-read and reusable automation scripts.
  • In addition to enabling scriptless automation, it helps a wide range of software testers to develop automation scripts as it supports numerous programming languages.
  • Its Hybrid Engine helps widen the test coverage by using an AI-powered visual recognition to detect all the UI elements and execute a variety of actions.
  • The Data-Driven test feature that enables retrieving data from Excel worksheets and CSV files also helps in expanding the test coverage.
  • If the UI element cannot be identified using the traditional object identification approaches, TestComplete switches to an AI-powered object recognition mode. It identifies the element and manipulates user actions such as clicks, hovers, and touches to widen test coverage across platforms using Optical Character Recognition (OCR). Such features make it the ideal choice for testing charts, mainframes, PDFs, SAP, and other such technologies.
  • It makes it easy to automate HTML5 elements that include Shadow DOM and Custom Elements.
  • The in-depth test report logs can be exported into different formats such as HTML, JUNIT, or MHT based on the stakeholder’s preference.

Robotium

Robotium is also another great open-source test framework that can be effective when it comes to writing automated gray box testing cases for Android applications. Robotium is very much similar to Selenium, but it is built for Android using Java and the JUnit test structure. You can use Robotium to write function, system, and acceptance test scenarios for multiple Android activities. What makes Robotium a great choice for testing Android apps is that it supports various android features such as activities, toast, menus, and even context menus. You can use Robotium to test mobile apps where only the source code is available and also to test mobile apps where only the APK file is available. Robotium even provides you with the option of testing on real Android devices or on an emulator. As a mobile app testing company, we always recommend using real devices for all testing. But even if you are using an emulator, you can still make use of Robotium.

Benefits of Robotium
  • Robotium makes it easy to write solid test cases with minimal time because of the shorter code.
  • It also enables us to develop powerful test cases even if in situations where we know very little about the application under test.
  • The readability of test cases in Robotium is greatly improved when compared to the standard instrumentation tests.
  • It provides automatic timing and delays.
  • Robotium automatically follows the current activity, finds the views, etc…
  • Robotium can take very useful autonomous decisions like deciding when to scroll and so on.
  • No additional modifications are required to the Android platform.
  • Robotium exhibits fast test execution.
  • The run-time binding to GUI components enables their test cases to be more robust.
  • It also integrates smoothly with Maven or Ant.

Xamarin.UITest

Xamarin.UITest is another great C# based automation framework for performing UI Acceptance testing on Android and iOS applications using the Nunit framework. Xamarin.UITest achieves this by using a single shared codebase (i.e) The complete body of the source code that is needed by the given program or application to run is stored in a single place).

Features
  • It is an open-source cross-platform testing framework.
  • Backdoors can be used to ensure that all the texts in a given fixture have the same data.
  • UI tests can be categorized into tablet-specific and mobile-specific tests for the same application.
  • It has a predefined 15 seconds window for local tests and a 1-minute window for App Center tests before it throws a TimeoutException. You can even customize the waiting period based on your requirements.
  • The Xamarin.UITest Automation Library allows testers to interact with the application UI and perform end-user actions such as entering text in input fields, tapping buttons, and gestures.
  • The REPL (Read-Eval-Print-Loop) tool can be used to dynamically test expressions to evaluate, execute and log the results. The expressions can even be copy-pasted into your UI testing projects.
  • You can ensure that your tests don’t fail on App Center tests by employing the embedded files and included files methods that help include the files with your test upload.
  • Hybrid mobile apps that use Web Views to display the HTML are harder to test as you’ll need to find a reference to the Web View first and then to the DOM elements it contains. However Xamarin.UITest has APIs that can interact with Web Views in such a situation.

Calabash

Calabash is an open-source automation testing framework that is developed and maintained by the Xamarin team. Being developed by the same team it has similar features as Xamarin.UITest such as cross-platform testing of both native and hybrid apps. Calabash is based on Behavior Driven Development (BDD) which describes the application’s behavior. So Calabash tests are executed on real mobile devices to get accurate results. Feature definition files and Steps definition files are instrumental in achieving mobile automation in the Calabash framework. Calabash’s tests are written in Gherkin, backed by Ruby language code, and run in the context of the Cucumber Framework. It supports about 80 different natural language commands (controllers), and new controllers that can be implemented in Ruby or Java language.

Features
  • It is an open-source framework that can be used to test both native & hybrid Android and iOS apps.
  • Since it supports Cucumber, you can write tests in a natural language that can be easily understood by everyone on the team including business experts and non-technical QA. Additionally, it also creates a bridge that allows Cucumber tests to be run and validated on Android and iOS.
  • It can interact with apps like Espresso or XCTest.
  • Integration with continuous integration or continuous delivery tools such as Jenkins is easy.
  • It can work with any Ruby-based test framework.
  • Calabash provides real-time feedback and validation across many different factors like OS versions, hardware specifications, OEM customizations, chipsets, amount of memory, and real-environment conditions such as backend integrations and network conditions.

Ranorex Studio

Ranorex Studio is a GUI test automation framework that can be used to test mobile, desktop, and web-based applications. It uses standard programming languages such as VB.NET and C#. What sets Ranorex Studio from the rest is that it includes the Ranorex recorder, object repository, Ranorex spy, code editor, and debugger in a single environment. It also provides great XML-based UI test execution reports that include screenshots of failed test cases. In comparison, Ranorex is better at Recording, Replaying, and Editing User Performed Actions.

Features
  • Ranorex Studio is an open-source tool developed by Ranorex GmbH.
  • It supports technologies like Silverlight, .NET, Window forms, Java, SAP, WPF, HTML5, Flash, Flex, Windows Apps (Native/Hybrid), and iOS, Android
  • It runs on Microsoft Windows and Windows Server.
  • Xpath technology is used for the identification of objects.
  • Object-Based Capture or Replay Editor offers a simple procedure to create automated test scripts that enable even non-coders to create tests without any hurdles.
  • It can test and validate web applications across popular browsers like Chrome, Safari, Firefox, and Microsoft Edge
  • It can be used for regression testing in continuous build environments to find new software bugs much faster.
  • The reports generated by Ranorex can be used to easily reproduce bugs and maintain the tests as well.
  • It provides great support for older OS versions. (Supported on Android 2.2 & higher, and on iOS 5.1 & higher.)

SeeTest

SeeTest Automation is a fantastic mobile app testing automation tool that supports both Image-Based and Object-Based recognition. It begins by connecting a real mobile device and a corresponding emulator or a cloud device. Once the device is connected, the control buttons will appear at the bottom of the computer screen that allows you to record and edit scripts while performing a test scenario. It has its own reporting mechanism that contains screenshots and video recordings of test executions. It also helps to evaluate the execution reports and export the selected test code to any of the testing frameworks.

SeeTest has the commands for almost all the actions which are performed on a mobile device. It includes actions such as changing the device settings, interacting with a non-instrumented application (SMS, Contacts, Dialler), launching & killing the application life-cycle, and so on. These actions are enabled by SeeTest controlling the device’s springboard.

Features
  • Supports automation of iOS, Android, Windows Phone, and BlackBerry applications.
  • Provides client libraries for the languages such as Java, C#, Perl, Python in order to develop automation scripts.
  • The mobile device can be connected either by using USB, Wi-Fi, or the SeeTest cloud.
  • It supports testing frameworks like UFT, TestComplete, RFT, C#, JUnit, Python, and Perl.
  • It can test Android wearable devices with non-instrumented mode.
  • SeeTest can quickly and easily validate layouts on a mobile device.
  • The password encrypt feature allows you to create tests that access a real account without revealing the account’s credentials.

EggPlant

Eggplant is a GUI test automation tool that employs an image-based approach to GUI testing to test the application with better and faster execution. It can be used to validate functionality based on the user’s perspective and check the performance of the mobile app on any device or operating system. This software uses a two-system model where one is the Eggplant testing tool that is installed and runs on a Desktop, and the second is the System under test (Mobile device). It uses a virtual network computing connection to bridge the above two systems. Eggplant’s standout performance provides strong test composition, environment management, dynamic control, and result in analytics to JMeter’s existing scripting capability.

Features
  • Supports Mac, Windows, and Linux
  • It can be used for Mobile Testing, Cross-Platform Testing, Internet Application Testing, and Performance Testing
  • It can interact with any device of your choices such as a mobile, tablet, desktop, server, and Internet of Things devices
  • Integration with Application Lifecycle Management (ALM) software like HPE Quality Center, IBM Rational Quality Manager, Bamboo, IBM UrbanCode, and Jenkins is a big plus.
  • Eggplant version 11 was integrated with the OCR (Optical Character Recognition) engine and even introduced macOS X Lion support.
  • Version 12 introduced the functional user interface redesign, consolidating the suite interface and scaling search which allows for testing across different sizes of screens with the same image.
  • Eggplant version 14 has database integration via ODBC (Open Database Connectivity).
  • Version 15 started supporting Tables for keyword-driven testing and Turbo Capture for script recording. It is worth noting that the VNC server in version 15 for Android allows its users to test an Android Smartwatch.

Apptim

Apptim is a great open-source tool that can be used to test your mobile app’s performance. It can be used to automatically measure app render times, power consumption, and resource utilization while capturing vital data such as crashes and so on. It is a desktop application that can be used to test native Android apps on Windows and native iOS & Android apps on macOS. In addition to supporting both native Android & iOS mobile apps, it also provides its users with a web-based workspace where they can save testing reports and share them with the entire team. So let’s take a look at the standout features of Apptim.

Features

Apptim is very easy to set up all thanks to its automated installation and low storage space requirements.

It also becomes a viable tool on your phone for keeping all features operational at peak performance.

  • Apptim has a user-friendly UI that is suitable for experts and novice users.
  • It establishes the essenial thresholds for the mobile performance KPIs.
  • Apptim has great security features as it automatically detects malware or spyware, quarantines them, and deletes them.
  • It analyses and evaluates performance tests.
  • Apptim provides a detailed report on functionality along with repair options.
  • Reports on comparison functionality can also be obtained if needed.
  • It provides hassle-free mobile app usage that minimizes time wastage.
  • It effectively replaces standalone emulators by running automated tests that are remotely powered.
  • Based on the feature, it even has the ability to employ both online and offline configurations.
  • It provides unlimited OS usage and timely facility updates as well.

Perfecto

Perfecto is one of the best cloud-based testing tools that has the ability to create a constantly updating test environment that keeps up with the frequent mobile and browser releases. It does so by allowing its users to use the most latest Android & iOS platforms instantaneously. It even works great with the newest versions of popular browsers such as Chrome, Firefox, and Safari. Perfecto’s test analysis report makes it easy to identify the source of a test failure by employing root cause analysis. Now let’s take a look at the set of its standout features.

Features
  • Perfecto supports mock location for iOS.
  • It enables parallel test execution that can save loads of time.
  • Perfecto provides access to mobile settings.
  • Perfecto allows you to install an unlimited number of mobile apps.
  • It supports JIRA integration for reporting and managing all the bugs.
  • It can be used to automate native, web, and hybrid mobile applications.

Frank

Frank is an open-source library that can be used to perform functional tests for iOS mobile apps. Frank embeds an HTTP server into the test version of the mobile application that runs once the app is started. Through this internal HTTP server, the cucumber part of Frank will send commands to the app to remotely perform actions such as button taps, filling texts, or looking for UI elements that you want to see on specific views. It can be used to write structured text tests, acceptance tests, and requirements with the help of Cucumber.

Features
  • Frank includes a powerful “app inspector” known as Symbiote to get detailed information on the running app.
  • Getting the iOS app set up for Frank does not take more than 10 minutes.
  • It can also be used to write tests for Mac apps and the process of writing those tests is similar to writing tests for iOS apps.
  • It has pre-defined steps that can be used right away by the user.
  • It allows you to record the Video of your cucumber run.
  • You can run your tests on both Simulators and Real (Physical) Devices.
  • Its continuous integration support enables you to run your tests on every check-in.

Espresso

Espresso is an Android exclusive testing framework that has been developed by Google, the company that makes Android. It provides a simple and flexible API to automate the UI in an Android mobile app. Espresso tests can be written in both Java and Kotlin languages. It even supports both Android native view and web view. Though it is intended to be used by developers for testing the UI of Android mobile apps, its features make it one of the best mobile app testing tools as well.

Features
  • The tests are highly scalable and flexible.
  • Espresso is integrated with Android Studio Applications.
  • Test scripts are far more stable and faster than Appium when it comes to UI testing.
  • UI test scripts are customizable and easy to maintain as well.
  • It has the Espresso Test Recorder that can record our manual user interactions in the UI to create automated tests without any coding.
  • Espresso also interacts & automates with web elements inside the WebView components.

Mobile App Automation Testing

We are a mobile app testing company that understands the value that automation brings to the table. But we also know that automation is no easy task and that it requires a lot of effort to implement it successfully. Appium is one of the best automation tools that every tester must know when it comes to mobile app automation testing. So we will be exploring how to set up Appium and learn how to automate various gestures as well.

Appium Setup for IOS Device

Required Software
  • Appium version: 1.22.0
  • MAC OS/version used to run Appium: 11.6
  • Node.js version: v15.14.0
  • Xcode version: 13

Once the required tools & software are installed, enter the below list of command lines one by one in the terminal,

      $ npm install -g appium
      $ npm install -g appium-doctor
      $ brew install libimobiledevice --HEAD
      $ brew install ideviceinstaller
      $ npm install -g ios-deploy
      $ gem install xcpretty
      $ cd /Applications/Appium Server GUI.app/Contents/Resources/app/nodemodules/appium/nodemodules/appium-webdriveragent
      $ brew install carthage
      $ npm i -g webpack
      $ mkdir -p Resources/WebDriverAgent.bundle
      $ ./Scripts/bootstrap.sh -d
      

Once you have executed the above commands without any errors in the terminal, you’d have to install the ‘WebdriverAgent’ iOS app on a real device (iPhone).

  • Connect the iPhone to your MAC Machine.
  • Open the ‘WebdriverAgent’ folder using the Finder.
  • Appium Setup for IOS Device - How to do Mobile App Testing

  • Open the ‘WebDriverAgent.xcodeproj’ file from that folder to open the XCode on the webdriveragent project.
  • Webdriver Agent in Appium Setup for IOS Device

  • Select the connected device in the ‘WebdriverAgentLib’ which is next to the build icon.
  • Connecting the Device in WebdriverAgent

  • Select the WebdriverAgent Project and select the basic tab, choose the ‘iOS Deployment Target’ version as the same version of your device or the lower version of it.
  • iOs Deployment Target in Appium Setup for IOS Device

  • Create an Apple ID and add it to the XCode(XCode -> Preference -> Account Tab)
  • Creating Apple id - How to do Mobile App Testing

  • Select the Target as ‘WebdriverAgentLib’ and keep the bundle identifier as unique. Select the Team as ‘Apple ID’(Added in the Preference’s Account) and the Deployment target as the connected iPhone OS version.
  • Identifying the elements in iOs device

  • Select the Target as ‘WebDriverAgentRunner’ and keep the product bundle identifier as unique in the Build Settings tab and the Deployment target as the connected iPhone OS version.
  • Selecting Target version in iPhone OS version

  • Select the General Tab in the ‘WebDriverAgentRunner’ Targets and select the Team as ‘Apple ID’(Added in the Preference’s Account). The Signing Certificate should be displayed as ‘iPhone Developer’.
  • Signing Certificate display

  • Follow the above two steps for the rest of the listed targets.
  • Build the project by pressing the ‘Play’() button and the ‘Build Succeeded’ message will appear(Avoid the warnings).
  • Get the UDID of the connected Mobile.
  • Enter the given below command with your phone UDID in the terminal which has already opened the WebDriverAgent folder.
      $ xcodebuild -project WebDriverAgent.xcodeproj -scheme WebDriverAgentRunner -destination 'id=
      <YOUR_UDID>
      ’ test -allowProvisioningUpdates
      

  • You need to trust the developer in the device of the WebdriverAgent App (Settings → General → Device Management → Developer App → Trust Developer)
  • Run the above ‘xcodebuild’ code in the terminal. Once the below image is displayed in your terminal, you can stop the process by pressing ‘CTRL + C’.
  • The WebDriverAgent app will be installed on your device and you will be able to continue your testing in Appium. Use the below config in Appium while testing the mobile application.
      {
      "deviceName": "iPhone",
      "platformName": "iOS",
      "VERSION": "12.0.1",
      "autoGrantPermissions": true,
      "udid": “
      <YOURS>
      ”,
      "automationName": "XCUITest",
      "bundleId": "com.ios.message",
      "noReset": true,
      "useNewWDA": false,
      "bootstrapPath": "/usr/local/lib/node_modules/appium/node_modules/appium-xcuitest-driver/WebDriverAgent",
      "agentPath": "/usr/local/lib/node_modules/appium/node_modules/appium-xcuitest-driver/WebDriverAgent/WebDriverAgent.xcodeproj"
      }
      

How to Automate Gestures

Since every smartphone has a touch screen, gestures are the highly important input actions needed to use a mobile app. So in this section, you will learn how to perform mobile gestures using Appium.

Tap

Tap gesture invokes or selects an item the same way a user places a finger on the item and removes it immediately. You can define an element to select or define the coordinates where the tap has to happen.

      TouchAction touchAction = new TouchAction(driver);
      touchAction.tap(tapOptions()
      .withElement(element(androidElement)))
      .perform()
      

Tap using Coordinates
      TouchAction touchAction = new TouchAction(driver);
      touchAction.tap(PointOption.point(1280, 1013)).perform();
      

Long Press

Long press is commonly used to display the context menu or to show options related to an item. So when a user places their finger on an item and holds on for a second or two, it should show the options related to the item.

      TouchAction touchAction = new TouchAction(driver);
      touchAction. longPress(LongPressOptions.longPressOptions()
      .withElement (ElementOption.element (element)))
      .perform ();
      

Swipe

Swipe is also a common gesture that will be needed to test a mobile app’s functionality. You can do so by using the below code.

      TouchAction swipe = new TouchAction(driver)
      .press(PointOption.point(972,500))
      .waitAction(waitOptions(ofMillis(800)))
      .moveTo(PointOption.point(108,500))
      .release()
      .perform();
      

Swipe using element
      int startX = startElement.getLocation().getX() + (startElement.getSize().getWidth() / 2);
      int startY = startElement.getLocation().getY() + (startElement.getSize().getHeight() / 2);
      int endX = endElement.getLocation().getX() + (endElement.getSize().getWidth() / 2);
      int endY = endElement.getLocation().getY() + (endElement.getSize().getHeight() / 2);
      new TouchAction(driver)
      .press(point(startX,startY))
      .waitAction(waitOptions(ofMillis(1000)))
      .moveTo(point(endX, endY))
      .release().perform();
      

Drag & Drop

Drag & Drop is such a convenient feature that results in great functionalities. So let’s take a look at the snippet you’ll need to automate the gesture in Appium.

      TouchAction swipe = new TouchAction(driver)
      .press(ElementOption.element(element1))
      .waitAction(waitOptions(ofSeconds(2)))
      .moveTo(ElementOption.element(element2))
      .release()
      .perform();
      

MultiTouch

Some apps don’t allow or support simultaneous touch points on the screen. However, mobile gaming apps should support multiple touchpoints for the game to be usable. In that case, you can’t go ahead without testing it. So we have listed the snippets you’ll need to test using multiple touchpoints actions such as zooming in, zooming out, and pinching.

      TouchAction touchActionOne = new TouchAction();
      touchActionOne.press(PointOption.point(100, 100));
      touchActionOne.release();
      TouchAction touchActionTwo = new TouchAction();
      touchActionTwo.press(PointOption.point(200, 200));
      touchActionTwo.release();
      MultiTouchAction action = new MultiTouchAction();
      action.add(touchActionOne);
      action.add(touchActionTwo);
      action.perform();
      

Touch Gestures to Zoom In and Out on Google Maps
      WebElement map = driver.findElement (By.id ("<>"));
      final int x=map.getLocation().getX()+map.getSize().getWidth()/2;
      final int y= map.getLocation().getY()+map.getSize().getHeight()/2;
      Zoom
      TouchAction finger1= new TouchAction(driver);
      finger1.press(PointOption.point(x,y-10)).moveTo(PointOption.point(x,y-100));
      TouchAction finger2= new TouchAction(driver);
      finger2.press(PointOption.point(x,y+10)).moveTo(PointOption.point(x,y+100));
      MultiTouchAction action= new MultiTouchAction(driver);
      action.add(finger1).add(finger2).perform();
      Pinch
      TouchAction finger3= new TouchAction(driver);
      finger3.press(PointOption.point(x,y-100)).moveTo(PointOption.point(x,y-10));
      TouchAction finger4= new TouchAction(driver);
      finger4.press(PointOption.point(x,y+100)).moveTo(PointOption.point(x,y+10));
      MultiTouchAction action2= new MultiTouchAction(driver);
      action2.add(finger3).add(finger4).perform();
      

Mobile App Performance Testing

Both the highs and the lows matter when it comes to performance testing as the mobile app must be able to handle both large amounts of load and perform the different actions despite having limited resources. The mobile app market is very unique due to the large number of devices available in different price brackets. Apart from the smartphone’s capacity, factors such as latency, bandwidth, CPU usage, and so on also matter. So testing your mobile app’s performance on a wide range of high-end and low-end devices under real-world conditions is critical. Here are a few KPIs that will help you ensure your mobile app’s performance and stability.

Mobile App Performance testing KPIs

The Key Performance Indicators that we are going to explore have been chosen based on the years of experience we’ve had in mobile app testing.

Ensure Short Load Time

Did you know that around 61% of users expect their apps to start within 4 seconds and respond within 2 seconds? So if you keep your users waiting, there are high chances for the user to just close the app and not even try your app ever again. But it is not just about testing to see if the mobile app responds within 2 seconds. You have to ensure that there is a loading animation or some kind of effect that lets the user know that the action is in progress. A simple switch without any transition might make the app seem slow to the end-user.

Ensure Quick Render Time

Render time might seem very similar to the load time that we saw earlier, but the key difference here is that it is the time taken not just for the screen to load. Rather, it is the time taken by the mobile app screen to become usable. In other words, the user must be able to interact with the mobile app screen without any issues. For example, users will find it annoying if a text field doesn’t allow them to type even though it is visible. So you can perform a stopwatch test to measure the rendering time of the entire page and verify if it is in the acceptable range.

Check for Dropped Frames

FPS (Frames Per Second) is a metric that is used to measure the visual fluidity of moving images in general. The lower the frame rate, the less fluid the motion is. So it is vital for mobile app testers to ensure that the mobile app doesn’t drop any intended frames when it is being used. FPS is not just for gaming applications as seamless transitions and animations are important for all kinds of mobile apps to provide the best user experience. The optimal frame rate is 30 frames per second for regular mobile applications and 60 frames per second for gaming applications.

Avoid Latency & Jitter Issues

It is common knowledge that data is divided into multiple packets of information when they are transmitted over any connection. So the back and forth transaction of these packets of information has to happen rapidly for the mobile app to have low latency. Data-heavy files or audio/video files are general types of data that will experience latency issues. So make sure to test for such latency issues to ensure the best user experience.
Jitters are also very similar to latency issues. The difference here is that latency measures only the time it takes for the data packet transfer. Whereas, jitters denote the level of inconsistency in latency. Jitters are frequently caused due to factors such as network congestion, route changes, and so on.

Prevent API Request Latency Issues

Apart from the general latency issues, API request latency issues are also very important as mobile apps are heavily reliant on APIs for their various functionalities. API request latency is the total amount of time it takes for an API system to respond to an API call. You can measure the value by calculating the time it takes for a request to be received by the API gateway and returned to the same client. Such latency issues will result in slow load times that could severely impact your mobile app’s success.

Prevent CPU Choking

Since the CPU (Central Processing Unit) is the unit in charge of executing all of an application’s instructions, it is a shared resource. So if your app unnecessarily chokes the CPU, the user might experience sluggishness or battery drain on the whole and not just in your app. If using a slow app annoys the user, then an app that slows your smartphone down will definitely be uninstalled instantaneously. You can keep track of your mobile app’s CPU consumption by using the below ADB command.

Syntax: adb shell dumpsys cpuinfo “Package-Name”

Example: adb shell dumpsys cpuinfo ‘com.example.myapp’

CPU Usage in Mobile App Performance Testing

Mobile App Usability Testing

We would have already explored some mobile app usability and user experience focus points in the checklists section. We will be taking a deep dive now to understand the importance of mobile app usability testing and find out how to do it effectively as well. Assessing a mobile app’s usability in the development or programming phase is impossible as it can be achieved only through testing. So let’s find out how to do it.

How to Perform Usability Testing?

Identify real users or representatives of the target audience who will be able to evaluate your product in real-world use cases. Make sure to observe how the users are using your app in different scenarios and identify the areas of improvement for your team to work on. Finally, interview the real users or representatives with questionnaires and prepare a feedback report to share with your team.

Usability Testing will help you answer critical questions such as

1. Is the mobile app useful?

2. Does the mobile app add value to the target audience?

3. Is the mobile app easy to use?

4. Does the mobile app fulfill its purpose effectively and efficiently?

The User Experience Focus Points

The objective of performing usability testing for your mobile app is to ensure the best user experience. Here are the focus points that will help us achieve this goal.

Context

A tailor-made mobile app can help enhance the user experience, and the best way to do it is by understanding the purpose of the app and its targeted user base. You can do so by answering the following questions.

1. Who will use the app? What’s special, unique, or different about them?

2. Where will the app be used? Will there be different uses or needs in different countries or rooms?

3. When will the app be used? Will it be used differently at different times of the day or year?

4. What tasks will be performed using the app? What other alternatives are there to perform the same task?

5. What are your users trying to achieve using the app?

6. Which parts or functionalities of the app are used the most? What do the people using the app want it to do?

7. How is the app used by the end-user? Is the app used in the way that it was originally intended?

For example: Let’s say you have released your mobile app after testing it using emulators on developer machines that have good network connectivity. By doing so, you are missing out on the context of the environment in which the app will be used. So your mobile app might face issues in real-world conditions. That is why you should test your app in all network conditions where the users will use the app.

Input – There should always be multiple options for a user to give inputs. If your mobile app is in need of the user’s location, it should be able to get the required input either by using the device GPS or by allowing the user to pin the location or type the street address manually.

Output – The output of your mobile app goes more than what’s displayed onscreen. For example, if your mobile game doesn’t produce the needed sound effects on time, it will definitely debase the user experience.

Responsiveness – We have already established how important responsiveness is as nobody likes to use a slow app. But since certain heavy operations will need time for it to function, we can avoid showing a blank screen, frozen screen, or a loading icon by loading the screen component by component. It is evident here that responsiveness is not just about the speed at which the page is loaded. So make sure to show why you are making the users wait when it is busy loading heavy operations.

Connectivity – Nowadays, most apps require a connection to the server to retrieve data. If connectivity is disrupted during a transaction, your app should display the appropriate error message to avoid the user from assuming that the issue is with the app. In addition to that, the app must be able to resume from where he/she left or inform what happened to the failed transaction.

Resources – Your app should not drain the battery quickly or dump your phone’s memory with unnecessary files.

Benefits of Usability Testing

1. By performing usability testing, you can understand your users and set benchmarks and usability standards for future releases.

2. Usability testing can be used to boost sales as it validates if a product is simple enough to use to make the end-user happy. It will also aid in minimizing the number of support calls from users.

3. Identifying usability issues before an app release minimizes the risk.

Compatibility Testing

According to a statistic, there were a whopping 7.1 Billion smartphone users in the year 2021. It was also reported that this number is expected to grow in the coming years. So once your mobile app is launched, it goes without saying that users will use it on different devices and platforms. So performing compatibility testing to ensure that your mobile app is compatible across various different device configurations is important. Without that, the time and effort you spent on planning and developing your app will go in vain. Before we find out how to perform compatibility testing, let’s take a closer look at the different issues the lack of compatibility testing will cause.

Common Compatibility Issues:

  • Content will not fit well on devices with different screen sizes.
  • The look and feel of the User Interface (UI) might differ.
  • Scroll bar issues.
  • Frames, media content, or tables may be broken.
  • Different navigation methods might be required.
  • Installation and upgrade issues.
  • DOCTYPE errors.
  • Failure to detect outdated browsers.
  • Layout issues.

To identify these issues, you’ll need to perform compatibility testing on different devices, operating systems, and browser combinations. With the number of smartphone models and variants increasing day by day, compatibility testing will play a crucial role in the success of your mobile app.

How to do Mobile App Compatibility Testing

It goes without saying that it is impossible to perform compatibility testing to cover all the existing combinations. So, start by defining priorities after thoroughly analyzing the target audience, existing users, current markets, popular gadgets, product specifics, and business objectives. Once you have the list of high-priority combinations, use the below focus points to get the best compatibility testing results.

Version Testing – Ensures the mobile app is compatible with different versions of the mobile operating systems.

Hardware Testing – Test the mobile application with various hardware configurations such as sound, screen, sensors, indicators, connections, buttons, available memory, power management features, and connected devices such as modems, disc drives, and serial ports.

Software Testing – Test if your mobile app works with other related apps for functionalities like sharing and so on.

Network Testing – Test the mobile app under different network conditions such as 3G, 4G, 5G, and Wi-Fi.

Operating System Testing – Confirms the software app performs appropriately with different operating systems such as Android OS, Blackberry OS, Apple iOS, Windows mobile OS and so on.

Device Testing – Ensure the app is compatible with different types of peripheral devices connected through USB or Bluetooth. SD Card, and others.

Types of Compatibility Testing

There are basically two types of Compatibility testing.

Backward Compatibility Testing is the process of testing the mobile app on older versions of devices and operating systems to make sure that your app can run on them as well. For example, if your mobile app works on Android 11, it should also be backward compatible with a device running on Android 10.

Forward Compatibility Testing is the process of testing the mobile app with new and upcoming versions of hardware and software. Since this is the opposite of the previous type, an app that is working fine on Android 10 should also function well if the device updates to Android 11.

Conclusion

Knowing when a mobile app is ready for release is very essential when it comes to successful app deployment. That is why we had decided to create a categorized mobile app testing checklist as it would be helpful to concentrate on real-world concerns such as app interruptions, resource utilization, usability, and so on. Since there is more to mobile app testing than following a mobile app testing checklist, we have also covered the top mobile app testing tools that will help you achieve your goals. A mobile app testing company must be able to render effective app testing services to the client irrespective of the varying needs. So the mobile app testing company should have strong automation capabilities, follow the best mobile app testing practices, and be familiar with the important tools.