Testing an algorithm in Software Testing is crucial because it ensures the reliability, accuracy, and efficiency of the underlying logic in any software system. Algorithms often form the foundation of key functionalities, such as sorting, searching, or decision-making processes. Ensuring correctness is vital, as even minor flaws can lead to significant failures in dependent systems. Furthermore, algorithm testing allows developers to verify edge cases, boundary conditions, and scenarios that might not be immediately apparent, reducing the risk of errors in production.
Unlike standard software testing, which often focuses on user interfaces, workflows, and system behavior, algorithm testing dives deep into the core logic and computational efficiency. It evaluates not just whether the algorithm works, but how well it performs under varying input sizes, data distributions, or resource constraints. This type of testing usually happens during the development phase and continues through iterative refinements. It is especially critical in areas like machine learning, cryptography, or optimization problems, where precision and performance directly impact outcomes.
Let’s see a step-by-step guideline on how to test an algorithm effectively.
Inputs and Outputs: What kind of format do the inputs need to be, and how should the outputs appear?
Constraints: What rules or limits does the algorithm need to follow?
Performance Goals: Should the algorithm be quick, precise, or use resources wisely?
2. Plan Test Cases
Make a full list of test cases.
Add different scenarios to cover every situation.
Test both regular and edge cases.
Provide clear and specific details for each test case.
Make sure the test cases are simple to understand and follow.
Functional Tests: Test if the algorithm works correctly in regular situations.
Boundary Tests:Test the algorithm with extremely large or small input values.
Error Tests: Find out how the algorithm handles incorrect or unexpected inputs.
Create a test case matrix.
This will make sure that we check all situations in an organized manner.
3. Unit Testing
Split the algorithm into smaller sections or functions.
Check each section one by one.
Look at how each part functions based on your expectations.
Pretend some outside parts are not there to pay attention to how things work by themselves.
Tools such as JUnit for Java, PyTest for Python, and NUnit for .NET are great options. They help automate unit tests effectively.
4. Positive and Negative Testing
Positive Testing: Use inputs that the algorithm can understand. Make sure it produces the correct outputs.
Negative Testing: Provide incorrect inputs or unusual data. This tests the algorithm’s strength and how well it can deal with errors.
5. Test for Edge and Corner Cases
Edge cases and corner cases can show problems that are hard to spot. Here are a few examples:
No entries, such as an empty list or missing values.
The highest or lowest allowed numbers.
Inputs that are very repetitive or identical.
Inputs that make numbers exceed or drop below limits.
6. Performance Testing
Evaluate how well the algorithm performs when the input sizes increase.
Time Complexity: See how long the program takes to run. You can do this with tools or profiling libraries.
Space Complexity: Check how much memory the program uses while it is running.
Simulate scenarios with:
Use small inputs to see how well it performs.
Use large inputs to test how it scales under pressure.
Use random or difficult inputs to check its strength.
7. Consistency and Stability Testing
Run the implementation of an algorithm several times with the same inputs. This will help you see if the results are the same each time, which is an important aspect of algorithm testing. It is especially important for algorithms that use random elements, like randomized algorithms.
8. Stress Testing
Push the algorithm to its limits by:
Run it with large sets of data.
Simulate several users or requests (for algorithms that need many threads).
Test when resources are limited (like having less memory or less processing power).
9. Comparative Testing
Compare how well the algorithm works and the results it produces against:
Well-known benchmarks or reliable algorithms.
Results from various versions of the same algorithm.
10. Dynamic and Static Analysis
Dynamic Analysis: Run the algorithm and see how it operates. Look for issues like memory leaks, slow parts, or unexpected errors.
Static Analysis: Review the algorithm’s code without executing it. Identify problems such as syntax errors, unused variables, or areas that could improve.
Tools like SonarQube, ESLint, and static analyzers in IDEs can help with this.
11. Regression Testing
After you change or update the algorithm, you should run all the old test cases again. This will help you see if the new changes caused any bugs or changed how things work.
12. Fuzz Testing
Make many random inputs, some good and some bad, to discover hidden problems or crashes.
Fuzz testing is very helpful for algorithms that are key for security.
13. Integration Testing
Test the algorithm in the larger system. Ensure it works well with the other parts. Verifying that it shares data correctly is a crucial step. Make sure it does not slow down or create errors, while also assessing its scalability.
14. Real-World Simulation
Simulate real-life situations to see how the algorithm works. For example:
Test search methods with real questions from users.
Check recommendation systems with real user activity data.
15. User Acceptance Testing (UAT)
Get feedback from end-users or stakeholders. This will ensure that the algorithm meets their needs and provides the results you want.
16. Security Testing
Getting in without permission
Leaking data
Attacks by harmful software
Identity theft
Unsafe data sending
Exposed data
Attacks that happen from input, such as SQL injection or buffer overflow.
Accessing data that should not be available or data breaches.
Timing attacks or problems from side-channel techniques.
17. Test Automation
Use testing frameworks to automate tasks that you do a lot.
For example, you can run the algorithm with different test cases.
Integrate the test suite into a Continuous Integration/Continuous Deployment (CI/CD) pipeline.
Run tests automatically with every code change.
Tools like Selenium, JUnit, and PyTest can help make this job easier.
18. Logging and Debugging
Turn on detailed logging during testing. This will help you save the input, output, and the steps you took.
Logs are very helpful for finding issues when tests fail.
19. Document Test Results
Keep track of your test cases.
Write down the expected results and what you actually got.
Note any bugs you find.
Good documentation makes things clear.
It is useful for fixes or updates in the future.
20. Iterative Testing and Refinement
Testing is not a one-time task. You should test again as needs change or the algorithm grows. Doing this helps ensure it still meets your goals.
Examples for Testing Algorithms
1. Sorting Algorithm
Scenario: Testing a QuickSort algorithm.
Test Cases:
Functional Test: Check if an unsorted array [7, 3, 5, 2, 4] becomes [2, 3, 4, 5, 7].
Boundary Test: Test with an empty array [] or a single-element array [1].
Performance Test: Test with arrays of sizes ranging from 1 to 1,000,000 elements.
Edge Case: Test with an array of identical elements [5, 5, 5, 5].
2. Machine Learning Model
Scenario: Testing a spam detection algorithm.
Test Cases:
Positive Test: Provide a known spam message like “Win a free iPhone now!” and check if it is flagged as spam.
Negative Test: Use a legitimate email like “Meeting rescheduled to 3 PM” and confirm it is not flagged as spam.
Performance Test: Evaluate accuracy on a dataset of 100,000 emails with a mix of spam and non-spam.
Edge Case: Test with emails containing only emojis or random characters.
3. Pathfinding Algorithm
Scenario: Testing Dijkstra’s algorithm to find the shortest path.
Test Cases:
Functional Test: Verify the shortest path in a simple graph with 4 nodes.
Edge Case: Use a graph with no connections to see how the algorithm handles unreachable destinations.
Stress Test: Use a graph with 1,000,000 nodes and dense connections.
Comparative Test: Compare the result with another shortest-path algorithm like A*.
4. API Request HandlingScenario: Testing a rate-limiting algorithm for an API.
Test Cases:
Positive Test: Allow 100 requests per minute as defined by the limit.
Negative Test: Send 150 requests per minute and ensure the algorithm denies excess requests gracefully.
Security Test: Test with invalid tokens or SQL injection attempts in request headers.
5. Recommendation System
Scenario: Testing a movie recommendation algorithm.
Test Cases:
Functional Test: Provide a user profile with a history of liking action movies and verify the system recommends similar movies.
Performance Test: Evaluate recommendations for a database of 10,000 movies.
Real-World Simulation: Test using actual user data to check for relevance and personalization.
Edge Case: Provide a user with no history and see how the system suggests content.
6. Financial Transactions
Scenario: Testing an algorithm that calculates loan EMI (Equated Monthly Installments).
Test Cases:
Functional Test: Test with known inputs (loan amount, interest rate, tenure) and verify the EMI calculation.
Boundary Test: Test with very high or very low interest rates and tenure values.
Stress Test: Simulate thousands of concurrent calculations for different users.
Error Test: Provide invalid inputs, such as negative loan amounts or zero tenure, and ensure proper error handling.
7. Image Processing Algorithm
Scenario: Testing an edge-detection algorithm for images.
Test Cases:
Positive Test: Provide a clear image and check if edges are detected correctly.
Negative Test: Use a blank image to confirm no edges are detected.
Edge Case: Test with extremely large images or images with noise.
Performance Test: Measure processing time for high-resolution images.
Conclusion
Testing an algorithm, particularly in AI applications, is an ongoing and essential task. It ensures the algorithm performs effectively across various scenarios and identifies areas for improvement. To achieve this, it is important to conduct unit testing to verify the functionality of each part of the algorithm. Testing edge cases is crucial for understanding how the algorithm handles rare or extreme inputs. Additionally, evaluating its performance provides insights into its speed and efficiency, while usability analysis ensures it is user-friendly and intuitive. Performing code analysis can also highlight areas for optimization and refinement.
By following these steps, you can enhance the reliability, accuracy, and overall effectiveness of your algorithms. This process not only helps identify and resolve potential flaws but also ensures the algorithm is scalable and adaptable to future needs. For software developers and computer scientists, testing is a critical aspect of creating robust solutions. It ensures that algorithms function as intended, allowing for continuous improvement and scalability, especially when developing Java or other programming solutions.
Frequently Asked Questions
Why do we test algorithms?
We test algorithms to make sure they work correctly and perform efficiently. Testing helps confirm that the algorithm gives the right results for all kinds of inputs, including edge cases. It also ensures that the algorithm runs fast enough and uses resources like memory effectively.
By testing algorithms, we can find and fix problems early, improving the quality of the software. This is especially important when the algorithm is part of a larger system or needs to handle complex tasks reliably.
How to test algorithm performance?
To test algorithm performance, start by measuring its execution time to see how long it takes to complete tasks with different input sizes. Evaluate memory usage to ensure the algorithm efficiently uses system resources during execution. Test the algorithm with a variety of inputs, including small, large, and edge-case scenarios, to observe its behavior under different conditions. Compare the algorithm's performance with alternative solutions for the same problem to identify strengths or weaknesses. Finally, simulate real-world conditions to ensure the algorithm performs reliably and efficiently in practical applications.
How to test an algorithm in programming ?
To test an algorithm in programming, start by writing test cases that cover a variety of scenarios. This includes basic cases to check if the algorithm works as expected, edge cases to test how it handles unusual or boundary inputs, and invalid inputs to see how it deals with errors or unexpected situations. After writing the test cases, run them and compare the algorithm’s output to the expected results. If any issues arise, debug the algorithm and re-test it. You should also test performance by running the algorithm with different input sizes to ensure it performs efficiently.
Software testers employ various types of tests to ensure that an application or software is working as expected. Though both smoke and regression testing are types of tests that testers use for this very same objective to test every new build of an application, they each solve different purposes and are employed at different stages of software testing as well. If you are looking to find out what both of these tests are, how they differ, and when you can use them to ensure maximum quality with your testing, you’re at the right place. So let’s get started by defining both of these tests and then take a deeper dive to explore the difference between smoke and regression testing with a comparison table.
What is Smoke Testing?
Since software testing is an integral part of the Software Development Life Cycle (SDLC), we cannot simply start testing the software even though it is ready enough to be tested. But how will we know if it is ready or not? That is where smoke testing comes into play. It helps testers to determine if the primary/core functionalities of the software are stable enough and working properly to be tested. But it is important to note that smoke testing is not only done at the beginning to validate the first build. Rather, It will be performed after every new build. Being a leading software testing company in USAsoftware testing company in USA, we never start our testing process for a new build without performing smoke testing.
An Example
Let’s assume that you have to perform smoke testing for an E-Commerce application, the following aspects of the application will be tested.
Installation & Launch
Login Functionality
Adding/removing a product to the Cart
Adding/removing a product to the Wishlist
Completing a Purchase
How did Smoke Testing get its name?
The term ‘smoke testing’ initially originated in electronic hardware testing. The implication here was that the board being tested should be switched off If it starts to smoke when turned on. The smoke was regarded as a warning that further testing should not be completed.
What is Regression Testing?
A small misconception might arise if you think that Regression testing is the same as smoke testing because it is also tested after every new build. But the key difference between smoke and regression testing is that it is used to perform a more in-depth level of testing that goes beyond just the happy journey of a customer. It also helps to validate that the existing functionality or features of the application are not impacted by the recent change such as
Bug fixing
Addition or removal of features or modules
Alteration of the code or configuration
Change in the requirement
An Example
Let’s consider the same example of testing an eCommerce application and assume the smoke test has passed. So these below-mentioned aspects or features of this application are just a few of the many checks that will happen during regression testing to ensure that the new feature works well on its own as well as with the existing features.
In addition to checking the purchasing process, we would now check if the payment can be made using all the available options.
We would check if we can change the privacy settings of the wishlist if it can be shared with others, and if the items added to the wishlist reflect in regular search results as well.
When it comes to the account functionality, we would also check by saving multiple addresses, validate the order history feature, check if the user can download the receipt for their purchase, and so on.
If any changes or new features are implemented, then every feature including what was tested earlier too will be tested with an end-to-end approach.
Difference between Smoke and Regression testing
We have developed this generic process flow to help you understand how these two types of testing differ from each other. Kindly keep in mind that this process flow might vary on a client-to-client basis.
Having seen the core difference between smoke and regression testing and an example to emphasize them, you will now be in a position to easily understand this direct comparison.
S. No
Smoke Testing
Regression Testing
1
Smoke Testing Is Used To Check If A Build Of The Software Is Stable Enough For It To Be Tested.
Regression Testing Is Used To Verify If Any Recent Changes Have Impacted The Existing Functionality.
2
It Is Employed By Both Software Testers And Developers.
It Is Predominantly Used By Software Testers Alone.
3
It Is A Surface-Level Type Of Testing As It Is Focused Only On Certain Modules And Performed On Initial & Unverified Builds.
It Is A More In-Depth Type Of Testing As It Has An End-To-End Approach And Is Performed Only On Stable Builds.
4
Smoke Testing Is Always Done Before Regression Testing.
Regression Testing Is Performed During Various Phases Of Testing.
5
It Doesn’t Require Much Time Or Resources To Be Done.
It Requires A Lot Of Time, Resources, And Effort To Be Done.
6
It Is Purely An Acceptance Type Of Testing As It Validates The Build And Prevents Further Testing If It Doesn’t Pass.
Though Functionalities Are Tested, It Is Not A Purely Acceptance-Based Type Of Testing As Any Issues Found In This Phase Don’t Prevent The Application From Being Further Tested.
7
Smoke Testing Definitely Requires Documentation
Regression Testing May Or May Not Require Documentation Based On The Need.
Conclusion
We hope you now have a clear understanding of the difference between Smoke and Regression Testing. Though there are a few similarities, both are crucial tests that have their own purpose. But performing regression testing for a growing product can be a challenge to many as it requires the right personnel and a lot of effort. Being an experienced regression testing company, we have been able to perform regression testing in the most efficient way to ensure not just quicker time to market, but also to ensure quality on arrival. So we will be publishing more such informative content in the coming weeks and highly recommend you to subscribe to our newsletter.
Technology and its ever-evolving nature can be quite challenging to comprehend, especially if one isn’t technologically adept. Not to mention that with every innovation, new terminology comes up.
Today, we’ll be discussing the differences between web services and APIs, two terms that many tend to interchange mistakenly.
What Are Web Services?
Web services are software components that allow applications to communicate over the internet. They are like APIs that don’t require written code, but they do need additional web programming to work.
There are two types of web services: SOAP and REST. SOAP forms the base for web services that use structured data, such as XML and JSON. In this type of web service, a client accesses an endpoint, which it uses to get the data it needs. This data gets sent back to the client, using it for whatever purpose.
On the other hand, REST follows a more straightforward approach to web services. It doesn’t require any structured data and mainly functions when you only need a small and specific set of data.
What Are APIs?
Application Programming Interface (API) is a piece of code that allows new and existing software applications to interact with other applications. In addition, APIs make it possible to transfer data and content between different applications, allowing them to function the way you want them to.
You can think of an API as a way for third-party developers to access your product and build on it. If a company wants to create a new feature, it can use the API to access your company’s data.
You have likely come across this feature before. An excellent example would be the function that allows you to use your Facebook account to log in to other applications, like Spotify. Booking a flight through travel sites is also one example of using APIs in real life.
Which One Does Your Business Need?
APIs and web services sometimes overlap in that all web services are APIs, but not all APIs are web services. Furthermore, APIs can function online and offline while web services cannot.
Aside from these key distinctions, APIs and web services also differ in terms of functionality. The question now is, which one does your business need? If you’re searching for something that can perform one of the following, then you’re looking to have APIs for your business.
Provide information about a subject matter
Offer the ability to request information
Bind services
What Happens Next?
The first step is to find a software developer if you don’t have one in-house. They can take your business’s idea and create an application based on it. Your team should also finalize ideas for your web service during your search.
Having a solid idea to present makes it easier to collaborate with the software developer, who will likely offer their input on improving the final product. After extensive meetings, the software developer will work on your web service, and then you will need a quality assurance (QA) company to test it out.
QA companies have the programmers and resources to test your web service for flaws during the development phase. They can also try the application before its official release, providing feedback to the software developer. Working with a QA company is a must since you don’t want your web service going live only to find out later on that it has numerous bugs and security flaws.
Ending Note
Web services are programmatic interfaces for software components utilized to connect apps, sites, and various other services over the internet. They are designed to support the exchange of data between applications, and they send, receive, and process data over the web.
If you aren’t sure if you need web services, we recommend reviewing what a web service can do to see if it fits your business requirements. Additionally, you must keep in mind that working with developers during this endeavor can also make the process easier to understand.
Should you push forth with web service development, you need to keep in mind that a QA company can ensure the successful launch of your web services. Codoid is a leading quality assurance company that strives to implement the best possible strategies to check if your web service is ready for launch. Contact us today to get started!
Today, hardly anyone doubts the need for rigorous software testing. In fact, the experts agree: in 2015, a military airplane crashed into the ground, killing those on board. The evidence later showed that an error in the programming caused the crash. Without extensive tests to check every line of code, could this tragedy have been prevented? The experts seem to think so.
Most bugs are not tragic. However, when a company tries to save money on testing, maintenance costs usually skyrocket later on. In many countries, more and more software development companies realize this because the demand for both manual and automation QA testers is growing steadily.
Unfortunately, there is one problem with being a tester, and it is a big one: there just aren’t enough of them. With all the successful game franchises and new, exciting products constantly in development, more and more people are considering QA as a career choice, which means that the competition for jobs is getting fiercer. At the same time, more and more companies are offering premium wages for testers with proper skills; this creates an even greater incentive to learn how to test. And yet, before you quit your current job to become a full-time tester, you have to ask yourself, “what do I need to be a successful tester?”
Codoid, one of the best automation testing companies, answers this question:
Excellent Communication Skills
Although it may seem strange for a tester, communication is one of the essential skills for any software tester. After all, what would be the point of testing a product if no one told you whether it passed? If a tester cannot properly communicate what they have found to their team, that tester is of no use to the company. They will be of even less use if they cannot communicate what needs to be done. A tester who cannot write professional bug reports and clearly explain how to fix the bugs will cause endless frustration on the programmer’s part and messy, confusing code on the programmer’s output.
Curiosity
Programmers are too familiar with their code and too eager to move on to the next thing to look for bugs. Testers, however, have a more objective methodology: they know that even the tiniest slip-up can have disastrous consequences. This is why it is invaluable for testers to be curious. They need to be able to ask “what if?” and test every possible outcome, no matter how unlikely it might be.
Analytical Thinking
A tester needs to find errors in that sequence and infer how the test should be carried out to meet the desired results. A tester needs to see the big picture, not just the individual lines.
Attention to Detail
Testers are finders of small, seemingly trivial errors. To them, every detail matters. They will notice if a word is misspelled, a button is positioned in the wrong place, or a graphic is out of place. Although it is not very glamorous, it is an essential part of the job.
End-User Perspective
To effectively test a program, a tester needs to have an end-user perspective. What is the feature supposed to do? How does it work? And, more importantly, how is it supposed to make the users feel? A tester needs to imagine himself as a regular user and think like one. The whole point of testing is to ensure that the end-user is getting what he wants and needs.
Conclusion
It is not easy to become a tester, but it is hardly impossible. The only thing you need to do is ask yourself this: do I meet all the requirements? If yes, you’re on your way to a successful career. If not, maybe it’s time to start. Whether you decide to learn how to test on your own or at a school, numerous free resources are available online. You can begin with the basics: go over testing terms and definitions, perfect your test planning skills and learn how to use test scripts. As you get more comfortable with the material, start testing yourself: play a game and think about how to test it, and when you’re ready, you can start looking for work in QA companies.
Codoid is one of the most reputable automation testing companies that offer reliable services to various businesses and organizations. Contact us today to find out how we can help you!
HTML, expanded as HyperText Markup Language is used to define the structure of the content in a webpage. HTML has its origins dating back to 1980 and its first version was officially launched in 1993. Since HTML5 is the most recent version, we will be discussing the key differences and upgrades that were introduced in HTML5. We will be able to understand the advantages that HTML5 has to offer when we pit HTML vs HTML5. We will also be exploring the history of HTML to see how it has transformed over the years.
If in case you don’t know how HTML works, it actually has a series of elements/syntax which you can use to structure and present the different parts of the content like heading text, paragraph text, bullet points, images, and so on. So it basically instructs the browser how to display the text, image, and other elements of a webpage. HTML is now being used with CSS (Cascading Style Sheet) and JavaScript to make the browser more dynamic. As one of the leading Software Testing companies in the industry, we believe that a good understanding of HTML will definitely come to use one day.
HTML’s History
As stated earlier, the beginning of HTML traces all the way back to 1980. Physicist Tim Berners-Lee, a specialist at CERN (European Organization for Nuclear Research) proposed a new “hypertext” framework that can be used for sharing reports and documents. Ultimately, Tim Berners-Lee got together with renowned frameworks engineer Robert Cailliau to introduce the triumphant proposition called the World Wide Web (W3).
HTML 1.0
The main proper report of HTML was distributed in 1991 under the name ‘HTML Tags’. It is considered a PC relic in today’s world. Though this move was made in 1991, the initial authority proposition to change HTML into a standard was made in 1993 by the IETF (Internet Engineering Task Force). The first version of HTML is regarded as HTML 1.0 and it had 18 elements.
HTML 2.0
HTML did not boom immediately and it also lacked a lot of basic features such as changing the font, including images & tables, and so on. So in 1995, IETF arranged for a gathering to create the HTML 2.0 norm that retained all the elements from HTML 1.0 and added a few features as well. This is the last set of HTML guidelines that were distributed by IETF.
HTML 3.2
The HTML guidelines started getting distributed by another standard association called W3C (World Wide Web Consortium) instead of IETF from 1996. So on January 14, 1997, the HTML 3.2 variant was distributed by the W3C. It introduced a lot of great features like Java applets, text streams around pictures, and so on.
HTML 4.0
HTML 4.0 (A revised variant of the first distribution of December 18, 1997) was distributed on April 24, 1998. It came with significant additions like CSS templates, the ability to remember little projects or scripts for the sites, working on the openness of the planned pages, complex tables, and upgrades in the structure. A rather update came in the form of HTML 4.0.1 on December 24, 1999. It was a modification and update of the HTML 4.0 form, so it didn’t address a lot of critical aspects.
HTML 5.0
With the distribution of HTML 4.0.1, the normalization movement of HTML halted as the W3C zeroed in on the improvement of the XHTML standard. So in 2004, organizations like Apple, Mozilla, and Opera expressed their anxiety about the absence of W3C’s interest in HTML and decided to arrange another affiliation called the WHATWG (Web Hypertext Application Technology Working Group). Owing to the strength of the organization that made up WHATWG, W3C came back to the normalizing movement of HTML and distributed the initial HTML 5.0 drafts in March 2007 and the first authority draft on January 22, 2008. Corresponding to its movement with HTML, W3C also proceeded with the normalization of XHTML, a high-level adaptation of HTML.
HTML vs HTML5
HTML5 is no longer just a website builder, it has evolved to become an entire application builder on its own. Unlike older versions of HTML, which only allowed you to create primarily static sites that needed to be spiced up with CSS and JavaScript, HTML5 is much more dynamic and includes multimedia elements.
Audio and Video Tags
With the introduction of
Games & Interactive Media
Browsers can display interactive 3D graphics using the computer’s own graphic processor. The need for any extra software or plugin to run games or any interactive media in web browsers also has been eliminated.
Data Storage
HTML employed cookies to store temporary data. Whereas, HTML5 utilizes web SQL databases, web storage, and application cache for storing the data offline. Since it became possible to store the data on the user’s device, certain app functionalities will work properly even without having an active internet connection.
Vector Graphics
We were only able to add vector graphics in HTML by VML, Flash, Silverlight, and other such technologies. But HTML5 introduced the ‘svg’ tag that enables us to include vector graphics directly.
Mobile-Friendly
The ability of HTML5 to generate better forms, the elimination of the flash player dependency, and general improvements like the introduction of the header, footer, and nav that adds the much-needed structure to the website make HTML5 a more mobile-friendly option in this HTML vs HTML5 comparison.
Cross-platform support
Since HTML5 can be used for deployment across various platforms, it acts as a huge time and money saver during the development phase.
Drag and Drop
HTML5 made it possible for the objects on the page to move according to the user’s cursor movement. So HTML5 was also able to enable drag and drop effects that HTML lacked.
Font Options
The look of the webpages also greatly improved with HTML5 supporting more fonts that can be displayed in a wide array of colors and with different effects as well.
Character Encoding
The introduction of the charset attribute meant that the character encoding process which was long and complicated in HTML got simplified in HTML5.
Geolocation
It was not possible to track the geolocation of a user with HTML. But since HTML5 allows JavaScript to run in the browser, a user’s geolocation can be easily tracked using the JS GeoLocation API. So that’s another major win for HTML5 in this HTML vs HTML5 comparison.
Ping
In addition to that, the ping attribute that came in HTML5 makes it possible for a list of URLs to be notified if and when a user clicks on a particular hyperlink. So the geolocation tracking and such features make HTML5 more resourceful when it comes to tracking and monitoring usage.
Disadvantages of HTML5:
Now that we have seen the HTML vs HTML5 comparison, let’s explore the few downsides that need improvement.
Since HTML5 requires modern browsers to access it, users utilizing old versions of the browser will face issues while accessing a website. There are also browser compatibility issues. So users might get unnecessarily frustrated with the website though there are no issues.
The language is always a work in progress and so you would have to be constantly following for any changes that might cause issues to your site. Though we can benefit from improvements, practically it might work as a threat.
Since media licensing is an issue in HTML5, it cannot produce dynamic outputs. Though we can embed audio files, graphics, interactivities, and videos, we will not be able to use HD videos and rich visuals that will make us miss Adobe Flash.
The security elements offered are also very limited.
Conclusion
Despite the few advantages, there is no denying the fact that HTML5 is very easy to use in comparison to HTML all thanks to the simple syntaxes. A few HTML tags were even removed completely in the update and a few tags were changed to a different syntax. For example, ‘dir’ was changed to ‘ul’. We hope you had a good time reading our HTML vs HTML5 comparison and learned the key differences between the two as well. As one of the top QA companies, we will be constantly updating a lot of useful information through our blogs. So make sure to stay tuned to this space for more.
Not many know that there is a lot of difference between Quality Assurance and Software Testing. Though they both contribute towards better software quality, they differ in terms of goal, approach, and so on. To put it in simple terms, software testing is the process of finding bugs that already exist in the software. Whereas, Quality Assurance is the process of preventing the creation of such bugs and future problems. So we would first perform software testing to find all the bugs, then with the available data, we will be able to perform quality control to achieve quality assurance. As a leading QA company, we have been able to successfully test products and provide quality assurance to many of our clients. So in this blog, we will be focusing on the core differences between QA and Software testing and explore why Quality Assurance is important.
The Differences between QA and Software Testing
Quality Assurance is much broader in scope as covers every process, policy, tool, guideline, training, workflow, etc. that a team will need to meet with their quality objectives. So software testing is basically a sub-branch of the quality assurance process. Now let’s take a look at the tabulated version of the key differences between QA and Software Testing.
S. No
Software Testing
Quality Assurance
1
The Primary Objective Is To Make The Application Bug-Free.
The Primary Objective Is To Make The Application Meet The Stakeholder’s Expectations.
2
Performed Once The Code Has Been Written.
Involved In Every Step Of The Development Process.
3
Checks Or Validates The Behavior Of The Application.
Improves The Application’s Quality Using Various Methods, Tools, Policies, Etc.
4
It Is Reactive In Nature As It Is A Corrective Action.
It Is Proactive In Nature As It Is A Preventive Action.
5
Focused On The Product.
Focused On The Process.
6
Developers Or Testers Perform The Tests.
Quality Assurance Activities Are Followed By All. (Stakeholders, Business Analysts, Developers, And Testers)
The Importance of Quality Assurance
We know for a fact that lack of bugs alone doesn’t make a quality product. If we were to go by an analogy, we can consider a dish that has been cooked in accordance with the given recipe & procedure. But what if there was something wrong with the recipe or the cooking process itself? The consumer still may not like the food though everything went according to plan. Likewise, an end-user might not feel satisfied with your product though it is bug-free.
Moreover, believing that the software being developed will have zero errors is a fallacy. Since we are aware of the obvious result, is it not wiser to follow quality assurance methods to minimize the effects of any quality issues later down the pipeline? It is impossible for the software tester who receives the code with the bugs to have any control over the development process. So the more attention and effort you put into quality assurance, the easier it gets while testing a product.
So it is evident that creating a successful product without either of these two is not possible. Though companies might have an internal team for executing basic tests, it will not be enough. A dedicated QA team will be mandatory as we all know that quality cannot be tested into a product. If there are any doubts regarding the team’s expansion, one can always outsource their QA needs to one of the best outsourcing companies such as us to get the job done without breaking a sweat while still staying on budget. We hope you are now clear of the various differences between QA and software testing
