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API Chaining: Simplifying Complex API Requests

by Rajesh K | Sep 16, 2025 | API Testing, Blog, Latest Post | 15 comments

In today’s software, you will see that one task often needs help from more than one service. Have you ever thought about how apps carry out these steps so easily? A big part of the answer is API chaining. This helpful method links several API requests in a row. The result from one request goes right into the next one, without you needing to do anything extra. This makes complex actions much easier. It is also very important in automation testing. You can copy real user actions using just one automated chain of steps. With API chaining, your app can work in a simple, smart way where every step sets up the next through easy api requests.

• API chaining lets you link a few API requests, so they work together as one step-by-step process.
• The output from one API call is used by the next one in line. So, each API depends on what comes before it.
• You need tools like Postman and API gateways to set up and handle chaining API calls easily.
• API chaining helps with end-to-end testing. It shows if different services work well with each other.
• It helps find problems with how things connect early on. That way, applications are more reliable and strong.

Understanding API Chaining and Its Core Principles

At its core, api chaining means making a sequence of api calls that depend on each other. You can think of it like a relay race. One person hands to the next, but here, it is data that moves along. First, you do one api call. The answer you get is then sent into the next api. You then use that response for another api call, and keep going like this. In the end, the chaining of api calls helps you finish a bigger job in a smooth way.

This way works well for automated testing. It lets you test an entire workflow, not just single api requests. With chaining, you see how data moves between services. This helps you find issues early. The api gateway can handle this full workflow on the server. This makes things easier for the client app.

Now, let’s look at how this process works in a simple way. We will talk about the main ideas that you need to understand.

How API Chaining Works: Step-by-Step Breakdown

Running a sequence of API requests through chaining is simple to follow. It begins with the first API request. This one step starts the whole workflow. The response from this first API call is important. It gives you the data you need for the next API requests in the sequence.

For example, the process might look like this:

• Step 1: First Request: You send the first request to an API endpoint to set up a new user account. The server gets this request, works on it, and sends a response with a unique user ID in it.
• Step 2: Data Extraction: You take the user ID out from the response you get from your first request.
• Step 3: Second Request: You use the same user ID in the request body or in the URL to make a second request. You do this to get the user’s profile details from another endpoint.

This easy, three-step process shows how chaining can bring different api endpoints together as one unit. The main point is that the second call needs the first to finish and give its output. This makes the workflow with your endpoints automated and smooth.

Key Concepts: Data Passing, Dependencies, and Sequence

To master API chaining, you need to know about three key ideas. The first one is data passing. The second is dependencies. The third one is sequence. These three work together to make sure your chaining workflow runs well and does what you want it to do. This is how you make the api chaining strong and stable in your workflow.

The mechanics of chaining rely on these elements:

• Data Passing: This means taking some data from one API response, like an authentication token or a user id, and then using it in the next API request. This is what links the chain together in the workflow.
• Dependencies: Later API calls in the chain need the earlier calls to work first. If the first API call does not go through, the whole workflow does not work, because the needed data such as the user id does not get passed forward
• Sequence: You have to run the API calls in the right order. If you do not use the right sequence, the logic of the workflow will break. Making sure every API call goes in the proper order helps with validation of the process and keeps it working well.

It is important to manage these ideas well when you build strong chains. For security, you need to handle sensitive data like tokens with care. A good practice is to use environment variables or secure places to store them. You should always make sure you have the right authentication steps set up for every link in the chain.

What is API Chaining?

API chaining is a way in software development where you make several API calls, but you do them one after another in a set order. You do not make random or single requests. With chaining, each API call uses the result from the last call to work. This links all the api calls into one smooth workflow. So, the output from one API is used in the next one to finish one larger job. API chaining helps when there are many steps and each step needs to follow the one before it. This happens a lot in workflows in software development.

Think of this as making a multi-step process work on its own. For example, when you want to book a flight, the steps are to search for flights first, pick a seat next, and then pay. You need to make one API call for each action. By chaining these API calls, you connect the different endpoints together. This lets you use one smooth functionality. It makes things a lot easier for the client app, and it lowers the amount of manual work needed.

Let’s look at how you can use the Postman tool to do this in real life.

How to Create a Collection?

One simple way to begin with api chaining is to use Postman. Postman is a well-known tool for api testing. To start, you should put your api requests into a collection. A collection in Postman is a place where you can group api requests that are linked. This makes it easy to handle them and run them together.

Creating one is simple:

• In the Postman app, click the “New” button. Then choose “Collection.”



• Type a name that shows what the collection is for, like “User Workflow.” Click “Create.”.

After you make your collection, you will have your own space to start building your sequence. This is the base for setting up your chain API calls. Every request you need for your API workflow will stay here. You can set the order in which they go and manage any shared data needed to run the chain api calls or the whole API workflow.

Add 2 Requests in Postman

With your collection set up in Postman, you can now add each API call that you need for your workflow. Postman is a good REST API client, so this step is easy to do with it. Start with the first request, as this will begin the workflow and set things in motion.

Here’s how you can add two requests:

• First Request: Click “Add a request.” Name it “Create User.” Add the user creation URL and choose POST as the method. Running it will return a user ID.
• Second Request: Add another request called “Get User Details.” Use the ID from the first request to fetch the user’s details.

Right now, you have two different requests in your collection. The next thing you need to do is to link them by moving data from the first one to the second one. This step is what chaining is all about.

Use Environment variables to parameterize the value to be referred

To pass data between requests in Postman, you need to use environment variables. If you put things like IDs or tokens by hand, it is not the best way to do this. It is slow and makes things hard to change. Instead, environment variables let you keep and use data in a way that changes as you go, which works well for chaining your steps. They are also better for keeping important data safe.

Here’s how to set them up:

• Click the “eye” icon at the top-right corner of Postman to open the environment management section. Click “Add” to make a new environment and give it a name.
• In your new environment, you can set values you need several times. For example, you can make a variable named userID but leave its “Initial Value” and “Current Value” empty for now.

When you use {{userID}} in your request URL or in the request body, it tells Postman to get the value for this variable every time you run it. This way, you can send the same requests again and again. It also lets you get ready for data that changes, which you may get from the first call in your chain.

Update the Fetched Values in Environment Variables

After you run your first request, you need to catch what comes back and keep it in an environment variable. In Postman, you can do this by adding a bit of JavaScript code in the “Tests” tab for your request. This script will run after you get the response.

To change the userID variable, you can use this script:

• Parse the response: First, get the JSON response from the API call. Just type const responseData = pm.response.json(); to do it.
• Set the variable: Now get the ID from the the api response, and put it as an environment variable. Write pm.environment.set(“userID”, responseData.id); for this.

This easy script takes care of the main part of chaining. When you run the “Create User” request, it will save the new user’s id to the userID variable on its own. It is also a good spot to add some basic validation. This helps make sure the id was made the right way before you go on.

Run the Second Request

Now, your userID environment variable is set to update on its own. You can use this in your second request. This will finish the chaining process in Postman. Go to your “Get User Details” request and set it up.

Here’s how to finish the setup:

• In the URL space for the second request, use the variable you made before. For example, if your endpoint is api/users/{id}, then your URL in Postman should be api/users/{{userID}}.
• Make sure you pick your environment from the list at the top right.

When you run the collection in Postman, the tool sends the requests one after another. The first call makes a new user and keeps the user id for you. Then, the second request takes this id and uses it to get the user’s details. This simple workflow is a big part of api testing. It shows how you can set up an api system to run all steps in order with no manual work.

Step-by-Step Guide to Implementing API Chaining in Automation Testing

Adding API chaining to your automation testing plan can help make things faster and cover more ground. Instead of having a different test for each API, you can set up full workflows that act like real users. The main steps are to find the right workflow, set up the sequence of API calls, and handle the data that moves from one call to the next.

The key is to make your tests change based on what happens. Start with the first API call. Get the needed info from its reply, like an authentication token or an ID. You will then use this info in all the subsequent requests that need it. It is also good to have validation checks after every call. This helps you know the workflow is going right. This way, you check each API and see if they work well together.

Real-World Use Cases for API Chaining

API chaining is used a lot in modern web applications. It helps make the user experience feel smooth. Any time you do something online that has more than one step, like ordering a product or booking a trip, there will be a chain of API calls working together behind the scenes. This is how these apps connect the steps for you.

In software development, chaining is a key technique when you need to build complex features in a fast and smooth way. For example, when you want to make an online store checkout system, you have to check inventory, process a payment, and create a shipping order. When you use chaining for these steps, it helps you manage the whole workflow as one simple transaction. This makes the process more reliable and also better in performance.

These are a few ways the chaining method can be used. Now, let us look at some cases in more detail.

Multi-Step Data Retrieval in Web Applications

In today’s web applications, getting data can take several steps. Sometimes, you want to find user information and then get the user’s recent activity from another service. You don’t have to make your app take care of both api requests. The api gateway can be set up to do this for you.

This is a good way to use a sequence of API calls. The workflow can go like this.

• The client makes one request to the api gateway.
• The api gateway first talks to a user service to get profile details for this user.
• The gateway then takes an id from that answer and uses it to call the activity service. The activity service gives back recent orders.
• After this, the gateway puts both answers together and sends all the data back to the client in one payload.

This way makes things easier on the client side. The server will handle the steps, so it can be faster and there will be less wait time. It is a good way to bring data together from more than one place.

Automated Testing and Validation Scenarios

API chaining is key in good automated testing. It lets testers do more than basic checks. With chaining, testers can check all steps of a business process from start to finish. This way, you can see if all linked services in the API do what they are meant to do. By following a user’s path through the app, you make sure every part works together, and the validation is done in the right way.

Common testing situations that use chain API calls include the following:

• User Authentication: A workflow to log in a user, get a token, and then use that token for a protected resource.
• E-commerce Order: A workflow where you add an item to the cart, move to checkout, and then confirm the order.
• Data Lifecycle: A workflow to make a resource, change it, and then remove it, checking at each step to see how it is.

These tests help a lot in software development. They find bugs when parts in software come together. Rest Assured is one tool that lets you build these tests with Java. It is easy to use. If you add it to the CI/CD pipeline, it helps the whole process work better. So, you can catch problems early and keep things running smooth.

Tools and Platforms for Simplifying API Chaining

Automating Chains with Postman and Rest Assured

For teams that want to start automation, Postman and Rest Assured are both good tools. Postman is easy to use because it lets you set up tasks visually. With its Collection Runner, you can run a list of requests one after the other. You can also use scripts to move data from one step to the next and to check facts along the way.

On the other hand, Rest Assured is a Java tool that helps with test automation. You can use it to chain API calls right in your own Java code. This makes it good for use in a CI/CD setup. Rest Assured helps make automation and testing of your API easy for you and your team.

• With Postman: You set up and manage your requests in a clear way using collections. You also use environment variables to connect your requests.
• With Rest Assured: You need to write code for each request. You read the value you get back from the first response, then use that value to make and send the next request.

Both tools are good for setting up a chain of calls. Rest Assured works well if you want it in your development pipeline. Postman is easy to use, and it helps you make and test things fast.

Leveraging API Gateways for Seamless Orchestration

API gateways give a strong and easy way, on the server, to handle API chaining. The client app does not need to make several calls. The gateway will do that for the client. This is called orchestration. In this setup, the server gateway works like a guide for all your backend services.

Here’s how it typically works:

• You set up a new route on your API gateway.
• In that route’s setup, you pick a pipeline or order for backend endpoints. These endpoints will be called in a set order.

When a client sends one request to the gateway’s route, the gateway goes through the whole chain of calls. The response moves from one service to the next, step by step. For example, Apache APISIX lets you build custom plugins for these kinds of pipeline requests. This helps make client code easier, cuts down network trips, and keeps your backend setup flexible.

Conclusion

To sum up, API chaining is a strong method that can help make complex API requests easier. It helps you get data and set up automation faster. When you understand the basics and use a clear plan, you can make your workflow more simple. It also makes testing better, and you will see smooth data interactions between several services. Using API chaining helps improve performance and brings more order when you handle dependencies and sequences. If you want to know more about api requests, chaining, and how api chaining can help with automation and your workflow, feel free to ask for a free consultation. This way, you can find solutions made just for you.

Frequently Asked Questions

GraphQL API Testing: Strategies and Tools for Testers

by Rajesh K | May 16, 2025 | API Testing, Blog, Latest Post | 10 comments

GraphQL, a powerful query language for APIs, has transformed how developers interact with data by allowing clients to request precisely what they need through a single endpoint. Unlike REST APIs, which rely on multiple fixed endpoints, GraphQL uses a strongly typed schema to define available data and operations, enabling flexible queries and mutations. This flexibility reduces data over-fetching and under-fetching, making APIs more efficient. However, it also introduces unique challenges that require a specialized approach to GraphQL API testing and software testing in general to ensure reliability, performance, and security. The dynamic nature of GraphQL queries, where clients can request arbitrary combinations of fields, demands a shift from traditional REST testing approaches. QA engineers must account for nested data structures, complex query patterns, and security concerns like unauthorized access or excessive query depth. This blog explores the challenges of GraphQL API testing, outlines effective testing strategies, highlights essential tools, and shares best practices to help testers ensure robust GraphQL services. With a focus on originality and practical insights, this guide aims to equip testers with the knowledge to tackle GraphQL testing effectively.

What is GraphQL?

GraphQL is a query language for APIs and a runtime for executing those queries with existing data. Developed by Facebook in 2012 and released publicly in 2015, GraphQL provides a more efficient, powerful, and flexible alternative to REST. It allows clients to define the structure of the required data, and the server returns exactly that, nothing more, nothing less.

Why is GraphQL API Testing Important?

Given GraphQL’s dynamic nature, testing becomes crucial to ensure:

• Schema Integrity: Validating that the schema accurately represents the data models and business logic.
• Resolver Accuracy: Ensuring resolvers fetch and manipulate data correctly.
• Security: Preventing unauthorized access and safeguarding against vulnerabilities like injection attacks.
• Performance: Maintaining optimal response times, especially with complex nested queries.

Challenges in GraphQL API Testing

GraphQL’s flexibility, while a strength, creates several testing hurdles:

• Combinatorial Query Complexity: Clients can request any combination of fields defined in the schema, leading to an exponential number of possible query shapes. For instance, a query for a “User” type might request just the name or include nested fields like posts, comments, and followers. Testing all possible combinations is impractical, making it difficult to achieve comprehensive coverage.
• Nested Data and N+1 Problems: GraphQL queries often involve deeply nested data, such as fetching a user’s posts and each post’s comments. This can lead to the N+1 problem, where a single query triggers multiple database calls, impacting performance. Testers must verify that resolvers handle nested queries efficiently without excessive latency.
• Error Handling: Unlike REST, which uses HTTP status codes, GraphQL returns errors in a standardized “errors” array within the response body. Testers must ensure that invalid queries, missing arguments, or type mismatches produce clear, actionable error messages without crashing the system.
• Security and Authorization: GraphQL’s single endpoint exposes many fields, requiring fine-grained access control at the field or query level. Testers must verify that unauthorized users cannot access restricted data and that introspection (which reveals the schema) is appropriately restricted in production.
• Performance Variability: Queries can range from lightweight (e.g., fetching a single field) to resource-intensive (e.g., deeply nested or wide queries). Testers need to simulate diverse query patterns to ensure the API performs well under typical and stress conditions.

These challenges necessitate tailored testing strategies that address GraphQL’s unique characteristics while ensuring functional correctness and system reliability.

Tools for GraphQL API Testing

Key Strategies for Effective GraphQL API Testing

To overcome these challenges, QA engineers can adopt the following strategies, each targeting specific aspects of GraphQL APIs:

1. Query and Mutation Testing

Queries (for fetching data) and mutations (for modifying data) are the core operations in GraphQL. Each must be tested thoroughly to ensure correct data retrieval and manipulation. For example, consider a GraphQL API for a library system with a query to fetch book details:

query {
   book(id: "123") {
       title
       author
       publicationYear
   }
}

Testers should verify that valid queries return the expected fields (e.g., title: “The Great Gatsby”) and that invalid inputs (e.g., missing ID or non-existent book) produce appropriate errors. Similarly, for a mutation like adding a book:

mutation {
   addBook(input: { title: "New Book", author: "Jane Doe" }) {
       id
       title
   }
}

Tests should confirm that the mutation creates the book and returns the correct data. Edge cases, such as invalid inputs or duplicate entries, should also be tested to ensure robust error handling. Tools like Jest or Mocha can automate these tests by sending queries and asserting response values.

2. Schema Validation

The GraphQL schema serves as the contract between the client and server, defining available types, fields, and operations. Schema testing ensures that updates or changes do not break existing functionality. Testers can use introspection queries to retrieve the schema and verify that all expected types (e.g., Book, Author) and fields (e.g., title: String!) are present and correctly typed.

Automated schema validation tools, such as GraphQL Inspector, can compare schema versions to detect breaking changes, like removed fields or altered types. For example, if a field changes from String to String! (non-nullable), tests should flag this as a potential breaking change. Integrating schema checks into CI pipelines ensures that changes are caught early.

3. Error Handling Tests

Robust error handling is crucial for a reliable API. Testers should craft queries that intentionally trigger errors, such as:

query {
   book(id: "123") {
       titles  # Invalid field
   }
}

This should return an error like:

{
       "errors": [
       {
       "message": "Cannot query field \"titles\" on type \"Book\"",
       "extensions": { "code": "GRAPHQL_VALIDATION_FAILED" }
       }
       ]
       }

Tests should verify that errors are descriptive, include appropriate codes, and do not expose sensitive information. Negative test cases should also cover invalid arguments, null values, or injection attempts to ensure the API handles malformed inputs gracefully.

4. Security and Permission Testing

Security testing focuses on protecting the API from unauthorized access and misuse. Key areas include:

• Introspection Control: Verify that schema introspection is disabled or restricted in production to prevent attackers from discovering internal schema details.
• Field-Level Authorization: Test that sensitive fields (e.g., user email) are only accessible to authorized users. For example, an unauthenticated query for a user’s email should return an access-denied error.
• Query Complexity Limits: Test that the API enforces limits on query depth or complexity to prevent denial-of-service attacks from overly nested queries, such as:

query {
   user(id: "1") {
       posts {
           comments {
               author {
                   posts { comments { author { ... } } }
               }
           }
       }
   }
}

5. Performance and Load Testing

Performance testing evaluates how the API handles varying query loads. Testers should benchmark lightweight queries (e.g., fetching a single book) against heavy queries (e.g., fetching all books with nested authors and reviews). Tools like JMeter or k6 can simulate concurrent users and measure latency, throughput, and resource usage.

Load tests should include stress scenarios, such as high-traffic conditions or unoptimized queries, to verify that caching, batching (e.g., using DataLoader), or rate-limiting mechanisms work effectively. Monitoring response sizes is also critical, as large JSON payloads can impact network performance.

Example: GraphQL API Testing for a Bookstore

Objective: Validate the correct functioning of a book query, including both expected behavior and handling of schema violations.

Positive Scenario: Fetch Book Details with Reviews

GraphQL Query

query {
  book(id: "1") {
    title
    author
    reviews {
      rating
      comment
    }
  }
}

Expected Response

{
  "data": {
    "book": {
      "title": "1984",
      "author": "George Orwell",
      "reviews": [
        {
          "rating": 5,
          "comment": "A dystopian masterpiece."
        },
        {
          "rating": 4,
          "comment": "Thought-provoking and intense."
        }
      ]
    }
  }
}

Test Assertions

• HTTP status is 200 OK.
• data.book.title equals “1984”.
• data.book.reviews is an array containing objects with rating and comment.

Purpose & Validation

• Confirms that the API correctly retrieves structured nested data.
• Ensures relationships (book → reviews) resolve accurately.
• Validates field names, data types, and content integrity.

Negative Scenario: Invalid Field Request

GraphQL Query

query {
  book(id: "1") {
    title
    publisher  # 'publisher' is not a valid field on Book
  }
}

Expected Error Response

{
  "errors": [
    {
      "message": "Cannot query field \"publisher\" on type \"Book\".",
      "locations": [
        {
          "line": 4,
          "column": 5
        }
      ],
      "extensions": {
        "code": "GRAPHQL_VALIDATION_FAILED"
      }
    }
  ]
}

Test Assertions

• HTTP status is 200 OK (GraphQL uses the response body for errors).
• Response includes an errors array.
• Error message includes “Cannot query field \”publisher\” on type \”Book\”.”.
• extensions.code equals “GRAPHQL_VALIDATION_FAILED”.

Purpose & Validation

• Verifies that schema validation is enforced.
• Ensures non-existent fields are properly rejected.
• Confirms descriptive error handling without exposing internal details.

Best Practices for GraphQL API Testing

To maximize testing effectiveness, QA engineers should follow these best practices:

1. Adopt the Test Pyramid: Focus on numerous unit tests (e.g., schema and resolver tests), fewer integration tests (e.g., endpoint tests with a database), and minimal end-to-end tests to balance coverage and speed.

2. Prioritize Realistic Scenarios

: Test queries and mutations that reflect common client use cases first, such as retrieving user profiles or updating orders, before tackling edge cases.

3. Manage Test Data: Ensure test databases include sufficient interconnected data to support nested queries. Include edge cases like empty or null fields to test robustness.

4. Mock External Dependencies: Use stubs or mocks for external API calls to ensure repeatable, cost-effective tests. For example, mock a payment gateway response instead of hitting a live service.

5. Automate Testing: Integrate tests into CI/CD pipelines to catch issues early. Use tools like GraphQL Inspector for schema validation and Jest for query testing.

6. Monitor Performance: Regularly test and monitor API performance in staging environments, setting thresholds for acceptable latency and error rates.

7. Keep Documentation Updated: Ensure the schema and API documentation remain in sync, using introspection to verify that deprecated fields are handled correctly.

Conclusion

GraphQL’s flexibility and power make it a compelling choice for modern API development—but with that power comes a responsibility to ensure robustness, security, and performance through thorough testing. As we’ve explored, effective GraphQL API testing involves validating schema integrity, crafting diverse query and mutation tests, addressing nested data challenges, simulating real-world load, and safeguarding against security threats. The positive and negative testing scenarios detailed above highlight the importance of not only validating expected outcomes but also ensuring that your API handles errors gracefully and securely. At Codoid, we specialize in comprehensive API testing services, including GraphQL. Our expert QA engineers leverage industry-leading tools and proven strategies to deliver highly reliable, secure, and scalable APIs for our clients. Whether you’re building a new GraphQL service or enhancing an existing one, our team can ensure that your API performs flawlessly in production environments.

Frequently Asked Questions

Supertest: The Ultimate Guide to Testing Node.js APIs

by Rajesh K | May 9, 2025 | API Testing, Blog, Latest Post | 21 comments

API testing is crucial for ensuring that your backend services work correctly and reliably. APIs often serve as the backbone of web and mobile applications, so catching bugs early through automated tests can save time and prevent costly issues in production. For Node.js developers and testers, the Supertest API library offers a powerful yet simple way to automate HTTP endpoint testing as part of your workflow. Supertest is a Node.js library (built on the Superagent HTTP client) designed specifically for testing web APIs. It allows you to simulate HTTP requests to your Node.js server and assert the responses without needing to run a browser or a separate client. This means you can test your RESTful endpoints directly in code, making it ideal for integration and end-to-end testing of your server logic. Developers and QA engineers favor Supertest because it is:

• Lightweight and code-driven – No GUI or separate app required, just JavaScript code.
• Seamlessly integrated with Node.js frameworks – Works great with Express or any Node HTTP server.
• Comprehensive – Lets you control headers, authentication, request payloads, and cookies in tests.
• CI/CD friendly – Easily runs in automated pipelines, returning standard exit codes on test pass/fail.
• Familiar to JavaScript developers – You write tests in JS/TS, using popular test frameworks like Jest or Mocha, so there’s no context-switching to a new language.

In this guide, we’ll walk through how to set up and use Supertest API for testing various HTTP methods (GET, POST, PUT, DELETE), validate responses (status codes, headers, and bodies), handle authentication, and even mock external API calls. We’ll also discuss how to integrate these tests into CI/CD pipelines and share best practices for effective API testing. By the end, you’ll be confident in writing robust API tests for your Node.js applications using Supertest.

Setting Up Supertest in Your Node.js Project

Before writing tests, you need to add Supertest to your project and set up a testing environment. Assuming you already have a Node.js application (for example, an Express app), follow these steps to get started:

• Install Supertest (and a test runner): Supertest is typically used with a testing framework like Jest or Mocha. If you don’t have a test runner set up, Jest is a popular choice for beginners due to its zero configuration. Install Supertest and Jest as development dependencies using npm:
  
  npm install --save-dev supertest jest
  
  

  This will add Supertest and Jest to your project’s node_modules. (If you prefer Mocha or another framework, you can install those instead of Jest.)

• Project Structure: Organize your tests in a dedicated directory. A common convention is to create a folder called tests or to put test files alongside your source files with a .test.js extension. For example:
  
  my-project/
  ├── app.js            # Your Express app or server
  └── tests/
      └── users.test.js # Your Supertest test file
  
  

  In this example, app.js exports an Express application (or Node HTTP server) which the tests will import. The test file users.test.js will contain our Supertest test cases.

• Configure the Test Script: If you’re using Jest, add a test script to your package.json (if not already present):
  
  "scripts": {
    "test": "jest"
  }
  
  

  This allows you to run all tests with the command npm test. (For Mocha, you might use “test”: “mocha” accordingly.)

With Supertest installed and your project structured for tests, you’re ready to write your first API test.

Writing Your First Supertest API Test

Let’s create a simple test to make sure everything is set up correctly. In your test file (e.g., users.test.js), you’ll require your app and the Supertest library, then define test cases. For example:

const request = require('supertest');    // import Supertest
const app = require('../app');           // import the Express app


describe('GET /api/users', () => {
  it('should return HTTP 200 and a list of users', async () => {
    const res = await request(app).get('/api/users');  // simulate GET request
    expect(res.statusCode).toBe(200);                  // assert status code is 200
    expect(res.body).toBeInstanceOf(Array);            // assert response body is an array
  });
});

In this test, request(app) creates a Supertest client for the Express app. We then call .get(‘/api/users’) and await the response. Finally, we use Jest’s expect to check that the status code is 200 (OK) and that the response body is an array (indicating a list of users).

Now, let’s dive deeper into testing various scenarios and features of an API using Supertest.

Testing Different HTTP Methods (GET, POST, PUT, DELETE)

Real-world APIs use multiple HTTP methods. Supertest makes it easy to test any request method by providing corresponding functions (.get(), .post(), .put(), .delete(), etc.) after calling request(app). Here’s how you can use Supertest for common HTTP methods:

// Examples of testing different HTTP methods with Supertest:

// GET request (fetch list of users)
await request(app)
  .get('/users')
  .expect(200);

// POST request (create a new user with JSON payload)
await request(app)
  .post('/users')
  .send({ name: 'John' })
  .expect(201);

// PUT request (update user with id 1)
await request(app)
  .put('/users/1')
  .send({ name: 'John Updated' })
  .expect(200);

// DELETE request (remove user with id 1)
await request(app)
  .delete('/users/1')
  .expect(204);

In the above snippet, each request is crafted for a specific endpoint and method:

• GET /users should return 200 OK (perhaps with a list of users).
• POST /users sends a JSON body ({ name: ‘John’ }) to create a new user. We expect a 201 Created status in response.
• PUT /users/1 sends an updated name for the user with ID 1 and expects a 200 OK for a successful update.
• DELETE /users/1 attempts to delete user 1 and expects a 204 No Content (a common response for successful deletions).

Notice the use of .send() for POST and PUT requests – this method attaches a request body. Supertest (via Superagent) automatically sets the Content-Type: application/json header when you pass an object to .send(). You can also chain an .expect(statusCode) to quickly assert the HTTP status.

Sending Data, Headers, and Query Parameters

When testing APIs, you often need to send data or custom headers, or verify endpoints with query parameters. Supertest provides ways to handle all of these:

• Query Parameters and URL Path Params: Include them in the URL string. For example:
  
  // GET /users?role=admin (query string)
  await request(app).get('/users?role=admin').expect(200);
  
  // GET /users/123 (path parameter)
  await request(app).get('/users/123').expect(200);
  
  

  If your route uses query parameters or dynamic URL segments, constructing the URL in the request call is straightforward.

• Request Body (JSON or form data): Use .send() for JSON payloads (as shown above). If you need to send form-url-encoded data or file uploads, Supertest (through Superagent) supports methods like .field() and .attach(). However, for most API tests sending JSON via .send({…}) is sufficient. Just ensure your server is configured (e.g., with body-parsing middleware) to handle the content type you send.
• Custom Headers: Use .set() to set any HTTP header on the request. Common examples include setting an Accept header or authorization tokens. For instance:
  
  await request(app)
    .post('/users')
    .send({ name: 'Alice' })
    .set('Accept', 'application/json')
    .expect('Content-Type', /json/)
    .expect(201);
  
  

  Here we set Accept: application/json to tell the server we expect a JSON response, and then we chain an expectation that the Content-Type of the response matches json. You can use .set() for any header your API might require (such as X-API-Key or custom headers).

Setting headers is also how you handle authentication in Supertest, which we’ll cover next.

Handling Authentication and Protected Routes

APIs often have protected endpoints that require authentication, such as a JSON Web Token (JWT) or an API key. To test these, you’ll need to include the appropriate auth credentials in your Supertest requests.

For example, if your API uses a Bearer token in the Authorization header (common with JWT-based auth), you can do:

const token = 'your-jwt-token-here';  // Typically you'd generate or retrieve this in your test setup
await request(app)
  .get('/dashboard')
  .set('Authorization', `Bearer ${token}`)
  .expect(200);

In this snippet, we set the Authorization header before making a GET request to a protected /dashboard route. We then expect a 200 OK if the token is valid and the user is authorized. If the token is missing or incorrect, you could test for a 401 Unauthorized or 403 Forbidden status accordingly.

Tip: In a real test scenario, you might first call a login endpoint (using Supertest) to retrieve a token, then use that token for subsequent requests. You can utilize Jest’s beforeAll hook to obtain auth tokens or set up any required state before running the secured-route tests, and an afterAll to clean up after tests (for example, invalidating a token or closing database connections).

Validating Responses: Status Codes, Bodies, and Headers

Supertest makes it easy to assert various parts of the HTTP response. We’ve already seen using .expect(STATUS) to check status codes, but you can also verify response headers and body content.

You can chain multiple Supertest .expect calls for convenient assertions. For example:

await request(app)
  .get('/users')
  .expect(200)                              // status code is 200
  .expect('Content-Type', /json/)           // Content-Type header contains "json"
  .expect(res => {
    // Custom assertion on response body
    if (!res.body.length) {
      throw new Error('No users found');
    }
  });

Here we chain three expectations:

• The response status should be 200.
• The Content-Type header should match a regex /json/ (indicating JSON content).
• A custom function that throws an error if the res.body array is empty (which would fail the test). This demonstrates how to do more complex assertions on the response body; if the condition inside .expect(res => { … }) is not met, the test will fail with that error.

Alternatively, you can always await the request and use your test framework’s assertion library on the response object. For example, with Jest you could do:

const res = await request(app).get('/users');
expect(res.statusCode).toBe(200);
expect(res.headers['content-type']).toMatch(/json/);
expect(res.body.length).toBeGreaterThan(0);

Both approaches are valid – choose the style you find more readable. Using Supertest’s chaining is concise for simple checks, whereas using your own expect calls on the res object can be more flexible for complex verification.

Testing Error Responses (Negative Testing)

It’s important to test not only the “happy path” but also how your API handles invalid input or error conditions. Supertest can help you simulate error scenarios and ensure your API responds correctly with the right status codes and messages.

For example, if your POST /users endpoint should return a 400 Bad Request when required fields are missing, you can write a test for that case:

it('should return 400 when required fields are missing', async () => {
  const res = await request(app)
    .post('/users')
    .send({});  // sending an empty body, assuming "name" or other fields are required
  expect(res.statusCode).toBe(400);
  // Optionally, check that an error message is returned in the body
  expect(res.body.error).toBeDefined();
});

In this test, we intentionally send an incomplete payload (empty object) to trigger a validation error. We then assert that the response status is 400. You could also assert on the response body (for example, checking that res.body.error or res.body.message contains the expected error info).

Similarly, you might test a 404 Not Found for a GET with a non-existent ID, or 401 Unauthorized when hitting a protected route without credentials. Covering these negative cases ensures your API fails gracefully and returns expected error codes that clients can handle.

Mocking External API Calls in Tests

Sometimes your API endpoints call third-party services (for example, an external REST API). In your tests, you might not want to hit the real external service (to avoid dependencies, flakiness, or side effects). This is where mocking comes in.

For Node.js, a popular library for mocking HTTP requests is Nock. Nock can intercept outgoing HTTP calls and simulate responses, which pairs nicely with Supertest when your code under test makes HTTP requests itself.

To use Nock, install it first:

npm install --save-dev nock

Then, in your tests, you can set up Nock before making the request with Supertest. For example:

// Mock the external API endpoint
nock('https://api.example.com')
  .get('/data')
  .reply(200, { result: 'ok' });


// Now make a request to your app (which calls the external API internally)
const res = await request(app).get('/internal-route');
expect(res.statusCode).toBe(200);
expect(res.body.result).toBe('ok');

In this way, when your application tries to reach api.example.com/data, Nock intercepts the call and returns the fake { result: ‘ok’ }. Our Supertest test then verifies that the app responded as expected without actually calling the real external service.

Best Practices for API Testing with Supertest

To get the most out of Supertest and keep your tests maintainable, consider the following best practices:

• Separate tests from application code: Keep your test files in a dedicated folder (like tests/) or use a naming convention like *.test.js. This makes it easier to manage code and ensures you don’t accidentally include test code in production builds. It also helps testing frameworks (like Jest) find your tests automatically.
• Use test data factories or generators: Instead of hardcoding data in your tests, generate dynamic data for more robust testing. For example, use libraries like Faker.js to create random user names, emails, etc. This can reveal issues that only occur with certain inputs and prevents all tests from using the exact same data. It keeps your tests closer to real-world scenarios.
• Test both success and failure paths: For each API endpoint, write tests for expected successful outcomes (200-range responses) and also for error conditions (4xx/5xx responses). Ensuring you have coverage for edge cases, bad inputs, and unauthorized access will make your API more reliable and bug-resistant.
• Clean up after tests: Tests should not leave the system in a dirty state. If your tests create or modify data (e.g., adding a user in the database), tear down that data at the end of the test or use setup/teardown hooks (beforeEach, afterEach) to reset state. This prevents tests from interfering with each other. Many testing frameworks allow you to reset database or app state between tests; use those features to isolate test cases.
• Use environment variables for configuration: Don’t hardcode sensitive values (like API keys, tokens, or database URLs) in your tests. Instead, use environment variables and perhaps a dedicated .env file for your test configuration. By using a package like dotenv, you can load test-specific environment variables (for example, pointing to a test database instead of production). This protects sensitive information and makes it easy to configure tests in different environments (local vs CI, etc.).

By following these practices, you’ll write tests that are cleaner, more reliable, and easier to maintain as your project grows.

Supertest vs Postman vs Rest Assured: Tool Comparison

While Supertest is a great tool for Node.js API testing, you might wonder how it stacks up against other popular API testing solutions like Postman or Rest Assured. Here’s a quick comparison:

In summary, Supertest shines for developers in the Node.js ecosystem who want to write programmatic tests alongside their application code. Postman is excellent for exploring and manually testing APIs (and it can do automation via Newman), but those tests live outside your codebase. Rest Assured is a powerful option for Java developers, but it isn’t applicable for Node.js apps. If you’re working with Node and want seamless integration with your development workflow and CI pipelines, Supertest is likely your best bet for API testing.

Conclusion

Automated API testing is a vital part of modern software development, and Supertest provides Node.js developers and testers with a robust, fast, and intuitive tool to achieve it. By integrating Supertest API tests into your development cycle, you can catch regressions early, ensure each endpoint behaves as intended, and refactor with confidence. We covered how to set up Supertest, write tests for various HTTP methods, handle things like headers, authentication, and external APIs, and even how to incorporate these tests into continuous integration pipelines.

Now it’s time to put this knowledge into practice. Set up Supertest in your Node.js project and start writing some tests for your own APIs. You’ll likely find that the effort pays off with more reliable code and faster debugging when things go wrong. Happy testing!

Frequently Asked Questions

Karate Framework for Simplified API Test Automation

by Rajesh K | Apr 5, 2025 | API Testing, Blog, Latest Post | 22 comments

API testing is a crucial component of modern software development, as it ensures that backend services and integrations function correctly, reliably, and securely. With the increasing complexity of distributed systems and microservices, validating API responses, performance, and behavior has become more important than ever. The Karate framework simplifies this process by offering a powerful and user-friendly platform that brings together API testing, automation, and assertions in a single framework. In this tutorial, we’ll walk you through how to set up and use Karate for API testing step by step. From installation to writing and executing your first test case, this guide is designed to help you get started with confidence. Whether you’re a beginner exploring API automation or an experienced tester looking for a simpler and more efficient framework, Karate provides the tools you need to build robust and maintainable API test automation.

What is the Karate Framework?

Karate is an open-source testing framework designed for API testing, API automation, and even UI testing. Unlike traditional tools that require extensive coding or complex scripting, Karate simplifies test creation by using a domain-specific language (DSL) based on Cucumber’s Gherkin syntax. This makes it easy for both developers and non-technical testers to write and execute test cases effortlessly.

With Karate, you can define API tests in plain-text (.feature) files, reducing the learning curve while ensuring readability and maintainability. It offers built-in assertions, data-driven testing, and seamless integration with CI/CD pipelines, making it a powerful choice for teams looking to streamline their automation efforts with minimal setup.

Prerequisites

Before we dive in, ensure you have the following:

• Java Development Kit (JDK): Version 8 or higher installed (Karate runs on Java).
• Maven: A build tool to manage dependencies (we’ll use it in this tutorial).
• An IDE: IntelliJ IDEA, Eclipse, or VS Code.
• A sample API: We’ll use the free Reqres for testing.

Let’s get started!

Step 1: Set Up Your Project

1. Create a New Maven Project

• If you’re using an IDE like IntelliJ, select “New Project” > “Maven” and click “Next.”
• Set the GroupId (e.g., org.example) and ArtifactId (e.g., KarateTutorial).

2. Configure the pom.xml File

Open your pom.xml and add the Karate dependency. Here’s a basic setup:

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
 <modelVersion>4.0.0</modelVersion>
 <groupId>org.example</groupId>
 <artifactId>KarateTutorial</artifactId>
 <version>1.0-SNAPSHOT</version>
 <name>Archetype - KarateTutorial</name>
 <url>http://maven.apache.org</url>




<properties>
 <maven.compiler.source>1.8</maven.compiler.source>
 <maven.compiler.target>1.8</maven.compiler.target>
 <karate.version>1.4.1</karate.version>
</properties>


<dependencies>
 <dependency>
   <groupId>com.intuit.karate</groupId>
   <artifactId>karate-junit5</artifactId>
   <version>${karate.version}</version>
   <scope>test</scope>
 </dependency>
</dependencies>


<build>
 <testResources>
   <testResource>
     <directory>src/test/java</directory>
     <includes>
       <include>**/*.feature</include>
     </includes>
   </testResource>
 </testResources>
 <plugins>
   <plugin>
     <groupId>org.apache.maven.plugins</groupId>
     <artifactId>maven-surefire-plugin</artifactId>
     <version>3.0.0-M5</version>
   </plugin>
 </plugins>
</build>
</project>

• This setup includes Karate with JUnit 5 integration and ensures that .feature files are recognized as test resources.

Sync the Project

• In your IDE, click “Reload Project” (Maven) to download the dependencies. If you’re using the command line, run mvn clean install.

Step 2: Create Your First Karate Test

1. Set Up the Directory Structure

• Inside src/test/java, create a folder called tests (e.g., src/test/java/tests).
• This is where we’ll store our .feature files.

2. Write a Simple Test

• Create a file named api_test.feature inside the tests folder.
• Add the following content:

Feature: Testing Reqres API with Karate


 Background:
   * url 'https://reqres.in'


 Scenario: Get a list of users
   Given path '/api/users?page=1'
   When method GET
   Then status 200
   And match response.page == 1
   And match response.per_page == 6
   And match response.total == 12
   And match response.total_pages == 2

Explanation:

• Feature: Describes the purpose of the test file.
• Scenario: A single test case.
• Given url: Sets the API endpoint.
• When method GET: Sends a GET request.
• Then status 200: Verifies the response status is 200 (OK).
• And match response.page == 1: Checks that the page value is equal to 1.

Step 3: Run the Test

1. Create a Test Runner

• In src/test/java/tests, create a Java file named ApiTestRunner.java:

package tests;


import com.intuit.karate.junit5.Karate;


class ApiTestRunner {
   @Karate.Test
   Karate testAll() {
       return Karate.run("api_test").relativeTo(getClass());
   }
}

• This runner tells Karate to execute the api_test.feature file.

Before Execution make sure the test folder looks like this.

2. Execute the Test

• Right-click ApiTestRunner.java and select “Run.”

You should see a report indicating the test passed, along with logs of the request and response.

Step 4: Expand Your Test Cases

Let’s add more scenarios to test different API functionalities.

1. Update api_test.feature

Replace the content with:

Feature: Testing Reqres API with Karate


 Background:

   * url 'https://reqres.in'


 Scenario: Get a list of users

   Given path '/api/users?page=1'

   When method GET

   Then status 200

   And match response.page == 1

   And match response.per_page == 6

   And match response.total == 12

   And match response.total_pages == 2


 Scenario: Get a single user by ID

   Given path '/api/users/2'

   When method GET

   Then status 200

   And match response.data.id == 2

   And match response.data.email == "[email protected]"

   And match response.data.first_name == "Janet"

   And match response.data.last_name == "Weaver"


 Scenario: Create a new post

   Given path 'api/users'

   And request {"name": "morpheus","job": "leader"}

   When method POST

   Then status 201

   And match response.name == "morpheus"

   And match response.job == "leader"

Explanation:

• Background: Defines a common setup (base URL) for all scenarios.
• First scenario: Tests GET request for a list of users.
• Second scenario: Tests GET request for a specific user.
• Third scenario: Tests POST request to create a resource.

Run the Updated Tests

• Use the same ApiTestRunner.java to execute the tests. You’ll see results for all three scenarios.

Step 5: Generate Reports

Karate automatically generates HTML reports.

1. Find the Report

• After running tests, check target/surefire-reports/karate-summary.html in your project folder.

• Open it in a browser to see a detailed summary of your test results.

Conclusion

Karate is a powerful yet simple framework that makes API automation accessible for both beginners and experienced testers. In this tutorial, we covered the essentials of API testing with Karate, including setting up a project, writing test cases, running tests, and generating reports. Unlike traditional API testing tools, Karate’s Gherkin-based syntax, built-in assertions, parallel execution, and seamless CI/CD integration allow teams to automate tests efficiently without extensive coding. Its data-driven testing and cross-functional capabilities make it an ideal choice for modern API automation. At Codoid, we specialize in API testing, UI automation, performance testing, and test automation consulting, helping businesses streamline their testing processes using tools like Karate, Selenium, and Cypress. Looking to optimize your API automation strategy? Codoid provides expert solutions to ensure seamless software quality—reach out to us today!

Frequently Asked Questions

Bruno API Automation: A Comprehensive Guide

by Hannah Rivera | Mar 17, 2025 | API Testing, Blog, Latest Post | 4 comments

The demand for robust and efficient API testing tools has never been higher. Teams are constantly seeking solutions that not only streamline their testing workflows but also integrate seamlessly with their existing development pipelines. Enter Bruno, a modern, open-source API client purpose-built for API testing and automation. Bruno distinguishes itself from traditional tools like Postman by offering a lightweight, local-first approach that prioritizes speed, security, and developer-friendly workflows. Designed to suit both individual testers and collaborative teams, Bruno brings simplicity and power to API automation by combining an intuitive interface with advanced features such as version control integration, JavaScript-based scripting, and command-line execution capabilities.

Unlike cloud-dependent platforms, Bruno emphasizes local-first architecture, meaning API collections and configurations are stored directly on your machine. This approach ensures data security and faster performance, enabling developers to easily sync test cases via Git or other version control systems. Additionally, Bruno offers flexible environment management and dynamic scripting to allow teams to build complex automated API workflows with minimal overhead. Bruno stands out as a compelling solution for organizations striving to modernize their API testing process and integrate automation into CI/CD pipelines. This guide explores Bruno’s setup, test case creation, scripting capabilities, environment configuration, and how it can enhance your API automation strategy.

Before you deep dive into API automation testing, we recommend checking out our detailed blog, which will give you valuable insights into how Bruno can optimize your API testing workflow.

Setting Up Bruno for API Automation

In the following sections, we’ll walk you through everything you need to get Bruno up and running for your API automation needs — from installation to creating your first automated test cases, configuring environment variables, and executing tests via CLI.

Whether you’re automating GET or POST requests, adding custom JavaScript assertions, or managing multiple environments, this guide will show you exactly how to harness Bruno’s capabilities to build a streamlined and efficient API testing workflow.

Install Bruno

• Download Bruno from its official site (bruno.io) or GitHub repository, depending on your OS.
• Follow the installation prompts to set up the tool on your computer.

Creating a Test Case Directory

Begin by launching the Bruno application and setting up a directory for storing test cases.

• 1. Run the Bruno application.
• 2. Create a COLLECTION named Testcase.
• 3. Select the project folder you created as the directory for this COLLECTION.

Writing and Executing Test Cases in Bruno

1. Creating a GET Request Test Case

• Click the ADD REQUEST button under the Testcase COLLECTION.
• Set the request type to GET.
• Name the request GetDemo and set the URL to:

https://jsonplaceholder.typicode.com/posts/1

2. Adding Test Assertions Using Built-in Assert

• Click the Assert button under the GetDemo request.
• Add the following assertions:
• Response status code equals 200.
• The title in the response body contains “provident.”



• Click Run in the upper right corner to execute the assertions.



3. Writing Test Assertions Using JavaScript

• Click the Tests button under GetDemo.
• Add the following assertions:
• Response status code equals 201.
• The title in the response body equals “foo.”
• Add the following script:



• Click Run to verify the assertions.

4. Creating a POST Request Test Case

• Click ADD REQUEST under the Testcase COLLECTION.
• Set the request type to POST.
• Name the request PostDemo and set the URL to:

https://jsonplaceholder.typicode.com/posts

• Click Body and enter the following JSON data:

5. Adding Test Assertions Using Built-in Assert

• Click the Assert button under PostDemo.
• Add the following assertions:
• Response status code equals 201.
• The title in the response body equals “foo.”



• Click Run to execute the assertions.

6. Writing Test Assertions Using JavaScript

• Click Tests under PostDemo.
• Add the following script:
test("res.status should be 201", function() {
 const data = res.getBody();
 expect(res.getStatus()).to.equal(201);
});
test("res.body should be correct", function() {
 const data = res.getBody();
 expect(data.title).to.equal('foo');
});

• Click Run to validate assertions.

Executing Two Test Cases Locally

• Click the Run button under the Testcase COLLECTION to execute all test cases.
• Check and validate whether the results match the expected outcomes.

Configuring Environment Variables in Bruno for API Testing

When running API test cases in different environments, modifying request addresses manually for each test case can be tedious, especially when dealing with multiple test cases. Bruno, an API testing tool, simplifies this by providing environment variables, allowing us to configure request addresses dynamically. This way, instead of modifying each test case, we can simply switch environments.

Creating Environment Variable Configuration Files in Bruno

Follow these steps to set up environment variables in Bruno:

1. Open the Environment Configuration Page:

• Click the Environments button under the Testcase COLLECTION to access the environment settings.

2. Create a New Environment:

• Click ADD ENVIRONMENT in the top-right corner.
• Enter a name for the environment (e.g., dev) and click SAVE to create the configuration file.

3. Add an Environment Variable:

• Select the newly created environment (dev) to open its configuration page.
• Click ADD VARIABLE in the top-right corner.
• Enter the variable name as host and set the value to https://jsonplaceholder.typicode.com.
• Click SAVE to apply the changes.

Using Environment Variables in Test Cases

Instead of hardcoding URLs in test cases, use {{host}} as a placeholder. Bruno will automatically replace it with the configured value from the selected environment, making it easy to switch between different testing environments.

By utilizing environment variables, you can streamline your API testing workflow, reducing manual effort and enhancing maintainability.

Using Environment Variables in Test Cases

Once environment variables are configured in Bruno, we can use them in test cases instead of hardcoding request addresses. This makes it easier to switch between different testing environments without modifying individual test cases.

Modifying Test Cases to Use Environment Variables

1. Update the GetDemo Request:

• Click the GetDemo request under the Testcase COLLECTION to open its editing page.
• Modify the request address to {{host}}/posts/1.
• Click SAVE to apply the changes.

2. Update the PostDemo Request:

• Click the PostDemo request under the Testcase COLLECTION to open its editing page.
• Modify the request address to {{host}}/posts.
• Click SAVE to apply the changes.

Debugging Environment Variables

• Click the Environments button under the Testcase COLLECTION and select the dev environment.
• Click the RUN button in the top-right corner to execute all test cases.
• Verify that the test results meet expectations.

Conclusion

Bruno is a lightweight and powerful tool designed for automating API testing with ease. Its local-first approach, Git-friendly structure, JavaScript-based scripting, and environment management make it ideal for building fast, secure, and reliable API tests. While Bruno streamlines automation, partnering with Codoid can help you take it further. As experts in API automation, Codoid provides end-to-end solutions to help you design, implement, and scale efficient testing frameworks integrated with your CI/CD pipelines. Reach out to Codoid today to enhance your API automation strategy and accelerate your software delivery.

Frequently Asked Questions

API Monitoring Guide: Optimize Performance & Ensure Reliability

by Chris Adams | Mar 10, 2025 | API Testing, Blog, Latest Post | 32 comments

APIs (Application Programming Interfaces) play a crucial role in enabling seamless communication between different systems, applications, and services. From web and mobile applications to cloud-based solutions, businesses rely heavily on APIs to deliver a smooth and efficient user experience. However, with this growing dependence comes the need for continuous monitoring to ensure APIs function optimally at all times. API monitoring is the process of tracking API performance, availability, and reliability in real-time, while API testing verifies that APIs function correctly, return expected responses, and meet performance benchmarks. Together, they ensure that APIs work as expected, respond within acceptable timeframes, and do not experience unexpected downtime or failures. Without proper monitoring and testing, even minor API failures can lead to service disruptions, frustrated users, and revenue losses. By proactively keeping an eye on API performance, businesses can ensure that their applications run smoothly, enhance user satisfaction, and maintain a competitive edge.

In this blog, we will explore the key aspects of API monitoring, its benefits, and best practices for keeping APIs reliable and high-performing. Whether you’re a developer, product manager, or business owner, understanding the significance of API monitoring is essential for delivering a top-notch digital experience.

Why API Monitoring is Important

• Detects Downtime Early: Alerts teams when an API is down or experiencing issues.
• Improves Performance: Helps identify slow response times or bottlenecks.
• Ensures Reliability: Monitors API endpoints to maintain a seamless experience for users.
• Enhances Security: Detects unusual traffic patterns or unauthorized access attempts.
• Optimizes Third-Party API Usage: Ensures external APIs used in applications are functioning correctly.

Types of API Monitoring

• Availability Monitoring: Checks if the API is online and accessible.
• Performance Monitoring: Measures response times, latency, and throughput.
• Functional Monitoring: Tests API endpoints to ensure they return correct responses.
• Security Monitoring: Detects vulnerabilities, unauthorized access, and potential attacks.
• Synthetic Monitoring: Simulates user behavior to test API responses under different conditions.
• Real User Monitoring (RUM): Tracks actual user interactions with the API in real-time.

Now that we’ve covered the types of API monitoring, let’s set it up using Postman. In the next section, we’ll go through the steps to configure test scripts, automate checks, and set up alerts for smooth API monitoring.

Set Up API Monitoring in Postman – A Step-by-Step Guide

Postman provides built-in API monitoring to help developers and testers track API performance, uptime, and response times. By automating API checks at scheduled intervals, Postman ensures that APIs remain functional, fast, and reliable.

Follow this step-by-step guide to set up API monitoring in Postman.

Step 1: Create a Postman Collection

A collection is a group of API requests that you want to monitor.

How to Create a Collection:

1. Open Postman and click on the “Collections” tab in the left sidebar.

2. Click “New Collection” and name it (e.g., “API Monitoring”).

3. Click “Add a request” and enter the API URL you want to monitor (e.g., https://api.example.com/users).

4. Select the request method (GET, POST, PUT, DELETE, etc.).

5. Click “Save” to store the request inside the collection.

Example:

• If you are monitoring a weather API, you might create a GET request like: https://api.weather.com/v1/location/{city}/forecas
• If you want to get single user from the list: https://reqres.in/api/users/2

Step 2: Add API Tests to Validate Responses

Postman allows you to write test scripts in JavaScript to validate API responses.

How to Add API Tests in Postman:

1. Open your saved API request from the collection.

2. Click on the “Tests” tab.

3. Enter the following test scripts to check API response time, status codes, and data validation.

Example Test Script:

// Check if API response time is under 500ms
pm.test("Response time is within limit", function () {
    pm.expect(pm.response.responseTime).to.be.below(500);
});

// Ensure the response returns HTTP 200 status
pm.test("Status code is 200", function () {
    pm.response.to.have.status(200);
});

// Validate that the response body contains specific data
pm.test("Response contains expected data", function () {
    var jsonData = pm.response.json();
    //pm.expect(jsonData.city).to.eql("New York");
     pm.expect(jsonData.data.first_name).to.eql("Janet");

});

4. Click “Save” to apply the tests to the request.

What These Tests Do:

• Response time check– Ensures API response is fast.
• Status code validation– Confirms API returns 200 OK.
• Data validation– Checks if the API response contains expected values.

Step 3: Configure Postman Monitor

Postman Monitors allow you to run API tests at scheduled intervals to check API health and performance.

How to Set Up a Monitor in Postman:

1. Click on the “Monitors” tab on the left sidebar.

2. Click “Create a Monitor” and select the collection you created earlier.

3. Set the monitoring frequency (e.g., every 5 minutes, hourly, or daily).

• Set the monitoring frequency (e.g., Every day, 12 AM , or daily).

4. Choose a region for monitoring (e.g., US East, Europe, Asia) to check API performance from different locations.

5. Click “Create Monitor” to start tracking API behavior.

Example: A company that operates globally might set up monitors to run every 10 minutes from different locations to detect regional API performance issues.

Step 4: Set Up Alerts for API Failures

To ensure quick response to API failures, Postman allows real-time notifications via email, Slack, and other integrations.

How to Set Up Alerts:

1. Open the Monitor settings in Postman.

2. Enable email notifications for failed tests.

3. Integrate Postman with Slack, Microsoft Teams, or PagerDuty for real-time alerts.

4. Use Postman Webhooks to send alerts to other monitoring systems.

Example: A fintech company might configure Slack alerts to notify developers immediately if their payment API fails.

Step 5: View API Monitoring Reports & Logs

Postman provides detailed execution history and logs to help you analyze API performance over time.

How to View Reports in Postman:

1. Click on the “Monitors” tab.

2. Select your API monitor to view logs.

3. Analyze:

• Success vs. failure rate of API calls.
• Average response time trends over time.
• Location-based API performance (if different regions were configured).

4. Export logs for debugging or reporting.

Example: A retail company might analyze logs to detect slow API response times during peak shopping hours and optimize their backend services.

Implementing API Monitoring Strategies

Implementing an effective API monitoring strategy involves setting up tools, defining key metrics, and ensuring proactive issue detection and resolution. Here’s a step-by-step approach:

1. Define API Monitoring Goals

Before implementing API monitoring, clarify the objectives:

• Ensure high availability (uptime monitoring).
• Improve performance (latency tracking).
• Validate functionality (response correctness).
• Detect security threats (unauthorized access or data leaks).
• Monitor third-party API dependencies (SLA compliance).

2. Identify Key API Metrics to Monitor

Track important API performance indicators, such as:

Availability Metrics

• Uptime/Downtime (Percentage of time API is available)
• Error Rate (5xx, 4xx errors)

Performance Metrics

• Response Time (Latency in milliseconds)
• Throughput (Requests per second)
• Rate Limiting Issues (Throttling by API providers)

Functional Metrics

• Payload Validation (Ensuring expected response structure)
• Endpoint Coverage (Monitoring all critical API endpoints)

Security Metrics

• Unauthorized Access Attempts
• Data Breach Indicators (Unusual data retrieval patterns)

3. Implement Different Types of Monitoring

A. Real-Time Monitoring

• Continuously check API health and trigger alerts if it fails.
• Use tools like Prometheus + Grafana for real-time metrics.

B. Synthetic API Testing

• Simulate real-world API calls and verify responses.
• Use Postman or Runscope to automate synthetic tests.

C. Log Analysis & Error Tracking

• Collect API logs and analyze patterns for failures.
• Use ELK Stack (Elasticsearch, Logstash, Kibana) or Datadog.

D. Load & Stress Testing

• Simulate heavy traffic to ensure APIs can handle peak loads.
• Use JMeter or k6 to test API scalability.

4. Set Up Automated Alerts & Notifications

• Use Slack, PagerDuty, or email alerts for incident notifications.
• Define thresholds (e.g., response time > 500ms, error rate > 2%).
• Use Prometheus AlertManager or Datadog Alerts for automation.

5. Integrate with CI/CD Pipelines

• Add API tests in Jenkins, GitHub Actions, or GitLab CI/CD.
• Run functional and performance tests during deployments.
• Prevent faulty API updates from going live.

6. Ensure API Security & Compliance

• Implement Rate Limiting & Authentication Checks.
• Monitor API for malicious requests (SQL injection, XSS, etc.).
• Ensure compliance with GDPR, HIPAA, or other regulations.

7. Regularly Review and Optimize Monitoring

• Conduct monthly API performance reviews.
• Adjust alert thresholds based on historical trends.
• Improve monitoring coverage for new API endpoints.

Conclusion

API monitoring helps prevent issues before they impact users. By using the right tools and strategies, businesses can minimize downtime, improve efficiency, and provide seamless digital experiences. To achieve robust API monitoring, expert guidance can make a significant difference. Codoid, a leading software testing company, provides comprehensive API testing and monitoring solutions, ensuring APIs function optimally under various conditions.

Frequently Asked Questions