Comprehensive and Beginner Friendly Guide to API Testing

“Quality is not an act, it is a habit.” – Aristotle

Picture this: You’re ordering a pizza using your favorite delivery app. You tap “Add to Cart,” and within seconds:

• the app updates your order
• calculates delivery charges
• and even suggests a side of garlic bread.

But how does this magic happen? Behind the scenes, APIs (Application Programming Interfaces) are hard at work, making sure your request reaches the restaurant, processes correctly, and delivers the right response back to your phone.

The user experience is very strongly coupled with APIs and how they respond to the request made by the client.

A client can be an app, a web browser, or even another API. The critical importance of API testing becomes evident when considering this responsibility, as APIs act as the bridge enabling seamless communication and interaction between these entities.

Before We Proceed

Let me brief some keywords which might buzz you as we proceed, if you are a beginner to the field. These are essential elements and also key components of API testing.

Base URL

The foundation of the API path, indicating the server or service being accessed.

? Example: https://api.openweathermap.org

? What It Represents: The domain or host where the API resides.

Endpoint

The specific resource or action you want to access within the API.

? Example: /data/2.5/weather

? What It Represents: The resource, such as weather data in this example.

Query Parameters

Additional information added to the API path to refine the request. These are key-value pairs appended to the URL after a ? and separated by &.

? Example: ?q=Berlin&appid=your_api_key

? What It Represents:

? q=Berlin: Specifies the city to fetch weather data for.

? appid=your_api_key: Your unique API key for authentication.

HTTP Method

The type of action you want to perform on the resource.

Examples from a classic CRUD cycle:

? GET: Retrieve data.

? POST: Create a new resource.

? PUT: Update an existing resource.

? DELETE: Remove a resource.

The method is not part of the path but is specified in the request.

Path Parameters (Optional)

Dynamic placeholders in the path, usually enclosed in curly braces {}.

? Example: /users/{user_id}/orders

? What It Represents:

? user_id: A dynamic value (e.g., /users/123/orders fetches orders for user 123).

Headers

Metadata included in the request, typically not visible in the URL but crucial for the request.

? Examples:

? Content-Type: application/json

? Authorization: Bearer <token>

Headers provide context or permissions needed by the API.

Authentication and Authorization

• Authentication: Verifying who the user or client is (e.g., login credentials).
• Authorization: Determining what the authenticated user is allowed to do (e.g., accessing specific resources or actions).

Plain English Example to Understand Authentication and Authorization

Imagine you’re at a cinema. Before entering, you must show your ticket at the entrance. The staff checks whether your ticket is valid (authentication). If it is, they confirm that you are indeed allowed to enter the cinema premises. If not, you’re kindly asked to leave or buy a ticket. Now that you’re inside the cinema with a valid ticket, you try to enter a specific screen. Let’s say you have a ticket for Oppenheimer, but you mistakenly or intentionally try to enter the Barbie screening. Here, the ticket checker at the screen verifies if your ticket allows you to watch Barbie (authorization). If not, you’ll be denied access(remember this term).

Authentication is about proving your identity—who you are. Authorization is about granting permissions—what you are allowed to do once authenticated.

Complete Example of an API Path

https://api.github.com/repos/{owner}/{repo}/issues?page=1&per_page=5        

Breakdown:

? Base URL: https://api.github.com (GitHub’s API).

? Endpoint: /repos/{owner}/{repo}/issues (Fetches issues for a specific repository).

? Path Parameters: {owner} and {repo} are dynamic placeholders for the repository owner and name.

? Query Parameters: page=1 to fetch the first page of results and per_page=5 will limit results to 5 issues per page.

Request and Response

? Request: This is what a client sends to the server to ask for data or trigger an action. It typically includes the URL, HTTP method, headers, and sometimes a body.

? Response: This is what the server sends back to the client, containing the requested data or a message indicating success, failure, or an error.

Response Time (Somehow Related to Latency)

The total time taken by the server to process a request and send back a response. Latency is a component of response time, referring to the delay caused by network factors before the server starts processing the request.

Testing an API

Preconditions(Starters)

• You have access to API documentation(which is supposed to be updated)
• Your test environment is setup

Test Cases(Main Course)

This is the core. The part where all your analytical and logical skills should come together. Your knowledge of product is necessary to write standalone API tests as well as the integration tests

Think of a client as the customer of your API. Clear error messages, adhering to correct HTTP status codes and faster response time will ensure that your API is self-explanatory for any use case.

Positive Test Cases

A positive test case is a type of test designed to verify that a system or component behaves as expected when provided with valid input or conditions.

The goal of a positive test case is to confirm that the application functions correctly and meets its requirements under normal operating circumstances.

Examples:

• User Management: You can create(register), use(login), update(change personal data) and delete user account. Doing all these C(create)R(read)U(update)D(delete) in same test is your E2E CRUD test case.
• Authentication: The newly created or existing use can login and access resources which are under its access level(authorization)
• Search Functionality: Allow users to search for resources based on specific criteria. If there are no results for a searched keyword, this is also a positive test(because user did nothing wrong).
• File Upload/Download: Enable users to upload and download files. These are usually POST and GET APIs.

Negative Test Cases

A negative test case is a type of test designed to validate that a system or component correctly handles invalid or unexpected input or conditions. This is where majority of your time will be consumed during testing an API.

The purpose of negative test cases is to ensure that the application can gracefully manage errors, exceptions, or adverse scenarios without crashing or producing incorrect results.

Invalid Request Data Format

These test case use data or conditions that are not valid, such as incorrect formats, null or empty, contains special characters etc. while it is not allowed.

Strings

Example of an email field in request:

? Input email: “existinguserexample.com” (not a valid email but still a valid string)
? Input email: “” (empty string. Should be caught on API request validation layer)
? Input email: null (same as second point)
? Missing Input from request        

Example of user's first name, last name or even full name fields:

? Ideally a name should not contain numbers i.e. "Akash1" is not valid
? Special Characters: "@Jane-Doe!" 
? Excessive Length: "A very very very long name that exceeds the maximum character limit"
? Invalid Format: "Doe, John" (last name first)
? Name Containing Offensive Language: Possible use case        

Integers

These examples demonstrate how to handle various invalid inputs for an integer datatype in API testing.

? Non-Numeric Input: Attempt to provide a string that cannot be converted to an integer. Let's say the request field age takes integer and we pass the value twenty-five instead of 25

? Negative Integer Where Positive is Expected: Request field score takes a value which is positive and API should respond with error when we pass -10. 
? Exceeding Maximum Integer Limit: Provide an integer that exceeds the maximum allowed value for the field. Example, 999999999999999999 exceedingly large integer.

? Missing Input from request
? Leading or Trailing Spaces like " 99 "
? Special Characters like "@199"        

Other such cases

• Invalid File Upload: Attempt to upload a file that exceeds size limits or is of an unsupported type.
• Malformed Request Payload: Send a request with a malformed JSON payload { "username": "user", "password": "pass" (missing closing brace)
• Invalid Query Parameters such as /api/products?sort=invalid

Valid Format but Data Does Not Exist

These are scenarios where the input format is valid, but the data being queried or submitted does not exist in the system.

These cases are essential to test because they help verify how the API handles situations where users provide correctly formatted requests that do not correspond to any existing records or resources.

Examples:

• Fetching User Profile by Non-Existent User ID
• Retrieving Order Details with Invalid Order Number
• Submitting Feedback for Non-Existent Product
• Fetching a resource which is previously deleted(the last validation step in an E2E CRUD test)

Authentication

These are scenarios involving invalid credentials during authentication.

These cases are crucial for verifying how the API handles incorrect or missing authentication information, ensuring that sensitive data is protected and that appropriate error messages are provided to the user.

• Login with Invalid Username or Password
• Invalid or expired access tokens
• Invalid refresh token used to generate a new access token
• Invalid auth header
• JWT token is valid but the payload used to generate the token is not relevant to the API. For example, JWT with payload { "role": "user", "username": "validUser" } is trying to access an admin endpoint which requires role to be admin.

Downtime Test Cases

These are the test cases that simulate API downtime or unavailability. These scenarios are crucial for understanding how the API behaves when it is down for maintenance, experiencing unexpected failures, undergoing network issues, or if the third party API is not behaving as expected.

Testing for downtime helps ensure that the system can handle failures gracefully and provides meaningful feedback to the user.

Examples:

• Simulating API Downtime with 503 Service Unavailable: Simulate a scenario where the API is intentionally taken down for maintenance.
• Timeout Scenario During API Call: Test how the API handles a request that times out due to server issues. This can be done either by using a mock server or by putting hard sleep in code.
• Network Failure Simulation: Simulate a network failure while trying to access the API.
• Backend Service Failure Affecting API: Simulate a scenario where a backend service that the API relies on is down(again a best use case for a mock)
• High Load Testing Leading to Downtime: Simulate a high load condition to test how the API handles excessive requests.
• Database Unavailability Affecting API Responses
• Cache Layer Downtime: For example, put Redis on sleep or pause and validate the timeouts in API and how it responds to the request.
• Failover Scenario for Downtime: Test how the API responds when switching to a backup server during downtime.
• Invalidate or delete the cache key while it is not expected

Rate Limits

These test cases focus on scenarios where an API is subjected to an excessive number of requests in a short period, exceeding its defined rate limits.

This is critical for ensuring the API can handle spikes in traffic and prevent abuse while maintaining performance for legitimate users.

Examples:

• Exceeding Rate Limit with Rapid Requests: Send a large number of requests in quick succession to exceed the API’s rate limit.

• Consistent Overload Over Time: Send a consistent stream of requests over an extended period, exceeding the daily limit.
• Testing Rate Limit Headers: Verify that the API returns appropriate rate limit headers when exceeding the limit(if applicable). Such as X-RateLimit-Limit: (maximum requests allowed), X-RateLimit-Remaining: 0, X-RateLimit-Reset: (timestamp of when limits reset)
• Testing Rate Limiting for Authenticated vs. Unauthenticated Requests: Compare rate limiting behavior between authenticated and unauthenticated requests.

HTTP Status Codes

HTTP status codes are crucial for understanding the responses your API provides to various requests.

HTTP status code indicates the outcome of an API call, helping clients determine whether a request was successful or if there were issues that need to be addressed.

While I am not a fan of memorizing status codes, I truly believe that this is QAs responsibility to make sure that an API adheres to standard HTTP codes.

HTTP status codes are the entry point for a test case, whether automated or manual. However, they should not be considered standalone test cases; further validations of the returned data are essential to ensure comprehensive testing.

Here’s a breakdown of the key categories of HTTP status codes along with some basic and common examples and their meanings.

Success API Calls(2xx)

• 200 OK: The request was successful, and the server returned the requested data.
• 201 Created: The request was successful, and a new resource was created as a result (commonly used for POST requests).

Client Error Responses (4xx)

• 400 Bad Request: Should be returned in cases we discussed for invalid format. This is an error that client can fix at their end.
• 404 Not Found: The server can’t find the requested resource (e.g., incorrect URL). This is valid for the cases we discussed where data is valid format but it does not exist(either we deleted it or it never existed).

Now, go back to the cinema example we have seen initially in this article. Then comeback here to check these two:

• 401 Unauthorized: The request requires user authentication. The client must authenticate itself to get the requested response.(Your entry in cinema depends on your ticket's validity).
• 403 Forbidden: The server understood the request, but it refuses to authorize it (your cinema ticket is valid but you are at a wrong screen).

Server Error Responses (5xx)

This is related to out downtime test cases.

• 500 Internal Server Error: The server encountered an unexpected condition that prevented it from fulfilling the request.
• 502 Bad Gateway: The server, while acting as a gateway or proxy, received an invalid response from the upstream server.
• 503 Service Unavailable: The server is currently unable to handle the request due to temporary overload or maintenance.

Plain english examples of 5xx status codes for better understanding:

500 Internal Server Error: Imagine you walk into a restaurant, and the chef suddenly trips and spills all the ingredients everywhere, making it impossible for them to prepare your meal. Similarly, a 500 Internal Server Error means that something unexpected happened on the server, preventing it from fulfilling your request.

502 Bad Gateway: Think of a post office trying to send a letter but receiving a faulty address from another post office. As a result, it can’t deliver your mail. A 502 Bad Gateway error occurs when the server, acting as a gateway or proxy, receives an invalid response from another server it was trying to contact.

503 Service Unavailable: Consider a popular bakery that runs out of ingredients and can’t make any more pastries for the day. When you arrive, they inform you that they can’t serve you right now. A 503 Service Unavailable error indicates that the server is temporarily unable to handle the request, often due to overload or maintenance.

504 Gateway Timeout: Imagine you’re waiting for a friend to pick you up, and you keep checking your watch. After waiting too long, you realize they must have gotten lost and can’t make it. A 504 Gateway Timeout error happens when one server doesn’t get a timely response from another server it was trying to reach, causing it to give up.

Messaging Queues and Caching

Messaging queues and caching are two important concepts in API design and performance optimization. They play a vital role in ensuring efficient data handling and improving the overall user experience.

Here’s an overview of each:

Messaging Queue

A messaging queue is a communication method used in software applications to send messages between different services or components asynchronously. It allows for decoupling of processes, meaning that the sender and receiver do not need to interact with each other directly or at the same time.

Asynchronously? Now what the hell is that? Let's me explain.

Imagine you send a text message to a friend while cooking dinner. You don’t wait for your friend to reply before you continue chopping vegetables. Instead, you keep cooking, and when your friend replies later, you check the message without interrupting your cooking. This is asynchronous communication—you’re not waiting for one action to finish before starting another.

Key Features of Messaging Queues:

Asynchronous Communication: Processes can continue without waiting for responses, improving system responsiveness.

Decoupling: Services can operate independently, which enhances scalability and maintainability.

Load Balancing: Messages can be distributed across multiple consumers, ensuring even processing of requests.

Messaging Queue in Testing

When testing APIs that utilize messaging queues, it’s important to verify that messages are properly sent, received, and processed. Here are some key aspects to consider:

Message Integrity: Ensure that messages sent to the queue maintain their integrity throughout the process. Test cases should validate that the content of the message is accurate and unaltered when it reaches the consumer.

Order of Processing: For certain applications, the order in which messages are processed can be crucial. Test cases should confirm that messages are consumed in the correct order, especially when the application depends on this sequence for functionality.

Error Handling: Test scenarios should include simulating failures in message processing to ensure the system can gracefully handle errors. This includes verifying that messages are retried or redirected to error queues when processing fails.

Performance Testing: Evaluate how well the messaging system handles high volumes of messages. Load testing can be performed to ensure that the system remains responsive and stable under heavy loads.

Caching

Caching is the process of storing frequently accessed data in a temporary storage area (cache) to improve the speed of data retrieval. By keeping copies of data that are expensive to fetch or compute, caching can significantly reduce latency and improve performance.

Key Features:

Faster Access: Caching provides quicker access to data compared to fetching it from the original source.

Reduced Load: By serving cached data, the load on the backend servers decreases, improving scalability.

Improved User Experience: Users experience faster response times, leading to higher satisfaction.

Think of a library where a librarian keeps a few popular books on a special shelf near the entrance for quick access. Instead of going through the entire library to find these books every time, visitors can quickly grab them from the shelf, making their experience faster and more enjoyable.

Caching in Testing

When testing APIs that implement caching, it’s important to validate the effectiveness and accuracy of the cache. Here are some considerations:

Cache Validity: Test cases should ensure that the cached data is valid and reflects the latest information. This involves verifying that updates to the underlying data are correctly reflected in the cache.

Cache Hit and Miss Rates: Assess the performance of the caching mechanism by measuring the hit (cached data retrieved) and miss (data retrieved from the original source) rates. This helps determine if the caching strategy is effective.

Expiration and Invalidation: Test scenarios should include checking the cache expiration settings and ensuring that stale data is not served to clients. Validate that the cache is invalidated when necessary, such as after updates to the underlying data.

Load Testing: Perform load testing to evaluate how caching impacts response times and server load. This helps ensure that the API can handle a large number of requests efficiently.

Best Practices for API Testing

Given the criticality and complexity of API testing, it is essential to conduct thorough pre-testing preparations to ensure success.

• Define Clear Test Cases: Create detailed test cases for both positive and negative scenarios to cover all aspects of API functionality.
• Avoid Assumptions: Do not assume anything unless it is explicitly stated in the requirements. Use your knowledge of the product to assess the impact of any changes on the integration layer, but rely on documented requirements to guide your testing.
• Seek Test Case Reviews: When in doubt, ask for test case reviews from developers, product managers, and fellow QA members.

Regular reviews often reveal missing test cases and improve overall test coverage.

• Automate tests: Implement test automation using tools like GitHub Actions and CI pipelines to run tests automatically. This saves precious time and energy, allowing you to focus on more complex testing scenarios.
• Monitor Production Performance and Logs: Engage in production monitoring and perform post-release sanity checks. Look for any edge cases that may lead to issues and ensure that everything is functioning as expected after deployment.
• Always Ensure Data Integrity: Validating that a resource is created in the correct state is essential for ensuring it can be consumed as a valid resource. This is a key aspect of end-to-end (E2E) validation.

By confirming data integrity, you help ensure that the entire system functions as intended and that users receive accurate and reliable information.

• Always Clean Up Your Test Data: Creating test data is a crucial part of testing, especially in API testing where a significant amount of data is generated. It’s important to ensure that you clean up this data afterward to prevent idle resources from cluttering your test environment.

Keeping things tidy not only helps maintain performance but also makes it easier to manage your testing process!

Common Pitfalls in API Testing

As APIs play a vital role in modern software development, their reliability and performance are critical for application success. However, the complexity of APIs and their integrations can lead to various challenges. Recognizing and avoiding these pitfalls is crucial for ensuring the effectiveness of API testing efforts.

Here are some common pitfalls that testers should be aware of:

Ignoring Security Testing: Overlooking security vulnerabilities can lead to significant risks; always include security tests.

Not Testing Edge Cases: Failing to account for edge cases can result in overlooked bugs; ensure comprehensive coverage.

Insufficient Documentation: Lack of proper documentation can hinder testing efforts; maintain clear and detailed API documentation.

Overlooking Rate Limits: Failing to test API rate limits can lead to service disruptions; always verify how your API behaves under stress.

Neglecting Dependency Management: Failing to account for dependencies on other services or APIs can lead to incomplete testing. Ensure that all integrations are considered when testing an API.

Tooling

Postman: A popular tool for building, testing, and documenting APIs. It provides an intuitive interface for creating requests and managing responses.

RestAssured: A powerful Java Domain-Specific Language (DSL) for automating REST APIs. Known for its reliability and simplicity,

RestAssured is one of the most popular tools for API automation. It allows testers to write clear and expressive tests, making it easier to validate API responses and functionality seamlessly.

JMeter: An open-source tool primarily used for performance testing APIs. It can simulate heavy loads and measure the performance under stress.

Apache Benchmark (ab): A command-line tool for measuring the performance of HTTP servers, Apache Benchmark is widely used for load testing APIs.

AB allows users to generate a significant amount of traffic to a server, helping to evaluate how well the API performs under different load conditions. All of this just from command line. Pretty impressive, right?

SoapUI: A comprehensive API testing tool for SOAP and REST services. It offers features for functional, security, and load testing.

Insomnia: A user-friendly API client that supports GraphQL and REST, allowing developers to design and test APIs easily.

Karate: A test automation framework that integrates API testing with BDD (Behavior-Driven Development) principles, enabling tests to be written in a more human-readable format.

Future Trends in API Testing

AI and Machine Learning: Increasing use of AI to enhance test automation and predict potential issues in APIs.

You can leverage AI to write the test cases and accept them if they seem relevant to you(remember the 'No Assumption' rule. Always review what you receive from AI)

GraphQL Adoption: Growing popularity of GraphQL APIs over REST, necessitating new testing strategies and tools.

Microservices Architecture: Microservices architecture is a design approach that structures an application as a collection of loosely coupled, independently deployable services. Each service is responsible for a specific business functionality and communicates with other services through well-defined APIs. This architecture promotes flexibility, scalability, and faster development cycles, making it increasingly popular in modern software development.

The shift towards microservices architecture necessitates a more robust and flexible approach to testing, focusing on integration, automation, and continuous monitoring to ensure the reliability and performance of interconnected services.

Continuous Testing: This approach aims to provide rapid feedback on the quality of the application by running automated tests throughout the development lifecycle, enabling teams to detect and address issues early.

In summary, continuous testing integrates API testing into CI/CD pipelines to provide rapid feedback and ensure that applications are thoroughly tested before deployment. This approach enhances collaboration, accelerates deployment cycles, and reduces risks, ultimately leading to higher-quality software delivered to users more efficiently.

In conclusion, API testing is a vital aspect of modern software development that ensures the reliability, performance, and security of applications. By understanding key concepts, avoiding common pitfalls, and implementing best practices, teams can effectively validate APIs and enhance the overall quality of their products. As technology continues to evolve, embracing continuous testing and leveraging powerful tools will further streamline the testing process, enabling organizations to deliver robust and user-friendly applications.

With a strong foundation in API testing, you’ll be well-equipped to tackle the challenges of today’s complex software landscape.

“An ounce of prevention is worth a pound of cure.” – Benjamin Franklin