APIs (Application Programming Interfaces) have become a fundamental element in modern application architecture. They enable systems to communicate with each other, facilitating the integration of third-party services, the creation of modular applications and access to external functionality.
However, to guarantee the proper operation and reliability of a PLC, it is essential to test it rigorously.
In this article, we explore the various uses of API testing to avoid the risks associated with API bugs, focusing on their importance, the types of tests to be carried out and best practices.
PLC testing involves checking PLCs both as individual software components and as integrated parts that fulfill the functionality required in the application.
As APIs manage the most critical sections of every application, each different testing stage includes API testing as a segment.
This can include writing test cases for API tests to be run manually or automatically to verify software performance, functionality, integration and security.
Different APIs may require different test cases, inputs/outputs and approaches.
For example, GraphQL APIs and gRPC APIs represent two distinct approaches to API design, and methods can differ considerably. However, for this article, we'll focus on general API test cases to consider.
APIs are the backbone of any application. They handle the critical aspects of a system to connect the backend to the frontend and keep operations running smoothly.
So it's only natural that creating and executing test cases for API testing is essential to guaranteeing a quality application.
API testing offers many advantages to developers, enabling them to :
The aim of this type of test is to check that the API is operating in accordance with its specifications. The aim is to ensure that the various requests (GET, POST, PUT, DELETE, etc.) return the expected results.
For example, for a user management API, a test could consist of sending a POST request to create a new user and checking that the response includes a 201 status code and the correct details of the user created.
Test cases :
Status code check: ensure that the PLC returns the correct status codes (e.g. 404).
Response payload validation: check whether the API response includes all the necessary fields and data values.
Error handling: check that API functional tests elegantly include error handling and provide meaningful error messages for invalid inputs.
Data accuracy: ensure that the API returns accurate and expected data.
CRUD operations: test the Create, Read, Update and Delete functions to make sure they work as expected.
The aim of load testing is to evaluate how the API behaves when called upon by a large number of simultaneous requests. This allows us to check the stability and scalability of the API under pressure.
For example, when selling tickets for a concert, a team could simulate 10,000 users simultaneously connecting to the API to make reservations.
Test cases :
Response time: measure PLC response time under normal and maximum loads.
Throughput: check the number of requests the API can handle per second.
Scalability: test how the API evolves with increasing load and simultaneous users.
Stress testing: evaluate PLC performance under extreme conditions to identify breaking points.
Load testing: simulate high user traffic to see how the API behaves under heavy load.
The aim of security testing is to identify potential vulnerabilities in the API.
This includes permission checking, authentication management, and protection against attacks such as SQL injections or XSS (cross-site scripting).
For example, a test could consist of trying to access a protected resource without the right authorizations to ensure that the API returns a 403 status code.
Test cases :
Authentication: check that the API requires appropriate authentication and correctly handles invalid credentials.
Authorization: ensure that users can only access resources they can view or modify.
Data encryption: check that sensitive data is encrypted during transmission.
Input validation: test for vulnerabilities such as SQL injection and ensure that the API correctly validates all inputs.
Rate limiting: check that the API applies rate limiting to prevent abuse and denial-of-service attacks.
When a new API version is released, regression testing ensures that existing functionality is not impacted by the changes.
For example, after a new feature has been added to a social network API, this can be used to check that the old features (profile creation, posting messages) are still working properly.
Test cases :
Basic comparison: compare current API responses with previous versions to ensure consistency.
Backward compatibility: check that new updates do not compromise existing functionality.
Functional test re-run: execute all functional test cases to ensure that no new bugs are introduced.
Integration testing: test API interactions with other services and systems to ensure they work properly.
Automated regression suite: maintain and run an automated test suite to quickly identify problems introduced by new changes.
This involvesidentifying critical API functionality, expected performance and security requirements.
This enables teams to prioritize the test scenarios to be automated, focusing on the most frequent use cases or those most likely to contain defects.
This includes checking the API's response to different scenarios and evaluating its behavior in the face of valid and invalid inputs.
Once validation has been carried out and confidence established in the API's operation, tests can be automated with complete confidence, reducing the risk of introducing errors into the automation process.
Tests containing complex logic or multiple dependencies can lead to false positives during automated execution.
So it's best to keep tests simple and straightforward, avoiding scenarios that require complex manipulation of data or reports.
Parallel test execution enables teams to evaluate the API on different browsers, devices and operating systems simultaneously.
This considerably reduces the time needed for testing, as several scenarios can be validated at the same time.
The right tool should offer features tailored to the team's specific needs, such as integration with version control systems, CI/CD tools and collaboration platforms.
This facilitates test management, enables better traceability and ensures that test automation is aligned with agile development practices.
Separating test data from automation scripts is a key practice that facilitates the reuse of test cases.
By storing test data in separate files or databases, teams can modify data without having to change scripts.
This enables testers to quickly update test scenarios or add new test cases with minimum effort.
This separation also simplifies data sharing between different scenarios and teams, making the automation process more flexible.
API testing is a crucial aspect of guaranteeing the quality, security and performance of an API over time, and must be integrated into the continuous development cycle, notably via CI/CD pipelines to automate testing and deploy updates with confidence.
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