What is the best way to prioritize API testing in your strategy?

API testing is not actually testing if you are not covering the DB validations. Just testing the API call and response wont be enough in the API Testing. DB level validation is actual validation point for the API testing. Just checking response code or GET API call for the validation is fine but its not fully validated.

Creating a high-level test plan early on that outlines the scope and coverage goals. example 1) Key user journeys to test end-to-end 2) Main API functions and expected behaviors-Priority areas like security, performance, reliability 3) Types of testing like load, functional, regression 4) Roles and responsibilities across test teams This test plan helps align everyone on what we aim to test, what's in scope, and how we'll approach it. It's not too detailed upfront, but captures the big picture. Having this high-level plan provides a nice guiding light for the test effort. It helps the team focus on the right areas as we dive into the details. Defining scope and objectives upfront has been really useful for my team.

In API testing, defining scope is vital, but a risk-based approach enhances resilience. Steps: Identify APIs by criticality. Define objectives considering risks. Prioritize APIs for function, security, UX. Assess and categorize risks. Allocate testing by risk priority. Focus on auth, errors, scenarios. Define endpoints and test data. Plan for performance, security, regression. Address compliance. Document and communicate. Continuous review and adapt. Embrace automation for efficiency.

APIs are the backbone of modern software applications -Educate your team and stakeholders about the significance of API testing. Emphasize that thorough API testing can prevent critical issues and improve the overall quality of your software. - Start API testing as early as possible in the development process. -Popular API testing tools like Postman, Swagger, and Rest Assured can be helpful for creating automated tests. -Focus on functional testing to verify that APIs perform the intended tasks correctly. Test different API endpoints, request parameters, and response data to ensure they meet the specified requirements. -In Automation, build reusable library components so it will be helpful for things to go in steady and faster pace

The first thing you should do is look at your application as a whole, and decide what not to test! Most modern applications are very complex, and usually interface with one or more 3rd party applications.

For example, I recently tested a checkout API that processes payments. It had high usage, some complex logic, and critical user impact if defective. I gave it a overall high risk score. This helped me prioritize rigorous testing and resources for the checkout API over lower risk APIs. Having that early risk matrix kept testing focused on the most critical APIs.

Prioritizing API testing through a risk-based approach involves assessing the potential risks associated with the APIs and allocating testing efforts accordingly. Here are steps to prioritize API testing using a risk-based approach: 1. Identify Risks 2. Categorize Risks 3. Assess Impact 4. Estimate Likelihood 5. Calculate Risk Priority 6. Define Testing Focus 7. Identify Test Scenarios 8. Determine Test Data 9. Plan for Mitigation 10. Include Security Testing 11. Performance and Load Testing 12. Regression Testing 13. Compliance and Regulatory Risks 14. Communication 15. Continuous Review This approach helps in efficient resource allocation and risk mitigation, ultimately leading to a more robust and reliable software product.

Once you have eliminated all the scenarios that do not apply to your application, the first thing to look at is any laws that apply to your industry. Even if your application works perfectly, but you break any law in production, quality of the product no longer matters. In a best case scenario, your company will be fined; worst case, your company will be shut down. Laws normally apply to protect consumer privacy and storing of private information. However, different industries might have additional regulations that apply only to them. For e.g. in the gambling industry, clients have to be not only over the age of consent, they also have to be physically within some geographical borders in order to be allowed to play.

In the realm of software testing, adopting a risk-based approach to prioritize API testing is paramount. This approach entails a comprehensive evaluation of the APIs under consideration, taking into account several key factors. First and foremost, it involves assessing the business impact of each API, identifying those that play a pivotal role in core business functions or revenue generation. Additionally, it scrutinizes usage frequency, shedding light on APIs that are frequently accessed and integral to user interactions or data flow. Equally critical is the evaluation of potential security vulnerabilities, especially for APIs handling sensitive data or interfacing with external systems.

One thing I've found helpful is to create a risk assessment template or matrix when first planning API testing. For each major API, I'll list factors like: 1) Usage - How frequently is the API called? What's the traffic? 2) Complexity - How complex is the API logic? How many dependencies? 3) Criticality - How critical is the API for user workflows? 4) Defect impact - What's the potential impact of defects in the API? 5) Then I'll assign rankings or scores to each factor and calculate an overall risk score.

One thing I've found really helpful is creating reusable test steps and utilities like: Pre-built authentication flows to get access tokens Test data generators Common test actions like health checks Shared assertion utilities JSON parsing helpers Building this reusable library of test building blocks allows me to automate faster. I can assemble test cases rapidly by reusing these common test steps and utilities. Updates get propagated everywhere too. It keeps my automated API tests DRY, maintainable, and efficient.

Automation is a game-changer in API testing. It not only reduces the manual effort but also ensures that tests are executed consistently. With automation tools like Postman or Rest-Assured, you can create detailed test suites that cover various scenarios, making it easier to catch regressions and edge cases. Additionally, automated tests can be integrated into your CI/CD pipeline, providing rapid feedback on code changes. This not only prioritizes API testing but also elevates the overall quality and reliability of your software.

The next area of your application to look at is money flow. It is of course important that you are not losing track of your customers' money (although that should already be covered under the legal category above), but it is equally important that your company is not losing money. Tests in this area should include any incentives that your company may offer to customers. In some cases, like in the hospitality industry, there are discounts for complicated packages that a customer may decide to purchase.

Automating API testing can significantly improve efficiency and enable prioritization through a risk-based approach. Here's how it can be prioritized API testing using test automation: 1. Identify High-Risk Areas 2. Define Test Scenarios 3. Automate High-Priority Tests 4. Leverage Test Automation Tools 5. Create Test Scripts 6. Parameterize Test Data 7. Establish Test Suites 8. Schedule Automated Tests 9. Implement Continuous Integration (CI) 10. Monitor Test Results 11. Ensure that automated tests are run as part of regression testing 12. When feasible, run automated tests in parallel to save time and increase test coverage 13. Maintain Test Scripts 14. Continuous Feedback 15. Ensure that your test automation framework is scalable

API automation, particularly via tools like Postman, is a cornerstone of modern software development and testing. Its significance lies in its ability to streamline and expedite testing processes, enhancing efficiency and accuracy. It excels in regression testing, ensuring that APIs remain robust as software evolves. With comprehensive test coverage, it thoroughly assesses diverse scenarios and edge cases. Automation in Postman integrates seamlessly with CI/CD pipelines, offering immediate feedback and preemptive issue identification. Load testing capabilities assess API performance under heavy usage, identifying scalability concerns. Data-driven testing enhances coverage, and parallel execution accelerates the process.

In the world of software testing, the Testing Pyramid offers a strategic framework to distribute testing efforts effectively. Comprising three layers—unit tests, integration tests, and end-to-end tests—it guides a balanced approach to API testing. Integration Tests - Middle Ground: Moving up, integration tests examine how components interact. Fewer in number than unit tests, they validate the cohesion of your API's various parts. Prioritizing API Testing: The Testing Pyramid emphasizes prioritizing API testing at lower levels for several reasons: . Reliability: Testing components in isolation ensures basic functionality reliability. Efficiency: Lower-level focus minimizes resource-intensive end-to-end testing needs.

One thing I've found that helps is first establishing a solid base of unit tests for the API code itself. For example, for REST API I'll unit test the request handling code, middleware, route handlers, etc. Then I'll focus on integration tests - common cases, validation, authentication, etc tested in isolation. Mocking services helps here. Finally, few broad end-to-end tests hitting the actual live API endpoints to validate the full flow. By having far more unit and targeted integration tests compared to full end-to-end, this can catch issues earlier, faster, and with more specific feedback. It makes iterating and debugging much easier. Following this pyramid has helped me optimize API testing efforts for greater confidence and efficiency.

Prioritizing API testing through the testing pyramid is a widely accepted approach that emphasizes a balanced distribution of testing efforts across different levels of the application. Here's how it can be followed the testing pyramid to prioritize API testing effectively: 1. Understand the Testing Pyramid 2. Prioritize Unit Testing 3. Comprehensive Unit Tests 4. Integration Testing 5. Test Interactions 6. End-to-End Testing 7. Validate User Scenarios 8. Automation Strategy 9. Performance Testing 10. Security Testing 11. Balance and Proportion 12. Continuous Integration (CI) 13. Continuous Monitoring 14. Adapt to Changes This approach helps maintain a balance between thorough testing and efficient resource utilization.

Follow the testing pyramid model, which suggests a balanced distribution of tests across unit, integration, and end-to-end levels. API testing falls within the integration testing layer.

With increasing technological complexity, especially in situations where systems are geographically separated, it is important to ask what happens to the data when things go wrong? Is the transmission retried? When? How often? How many points of failure are there? For example: When our payment processor goes offline (due to actual problems, or just maintenance), do we turn away all our customers? Do we assume to approve them for certain amount, and process all payments as a batch at a later time? Do any of these “emergency procedures” have legal implications?

Prioritizing API testing through regular review and updates is a dynamic approach that allows to adapt the testing strategy to the evolving needs of the application. Here's how it can be prioritized API testing through this method: 1. Establish a Testing Baseline 2. Identify High-Impact Areas 3. Review Existing Test Cases 4. Prioritize Critical Scenarios 5. Focus on Regression Testing 6. Security and Compliance 7. Performance and Scalability 8. Automate Critical Tests 9. Regularly Update Test Data 10. Continuous Monitoring 11. Collaborate with Developers 12. Adjust Test Scope as Needed 13. Document Changes 14. Reevaluate Regularly 15. Continuous Improvement

API testing requires continuous review and update as the APIs evolve.One technique i have found useful is to review and update my API test suites after any major deployment. For example, after we release a new version of an API, will work with the development team to understand what changed then methodically go through related test cases and update them to match the new behavior. Following things to look for 1) New response fields to assert 2) Updated validation rules 3) Changed error messages 4) Deprecated endpoints By regularly reviewing tests against recent deployments in this way, I can keep my test automation up-to-date and accurate. It's a bit of overhead but pays off in preventing regressions and improving test stability over time

Regressions should be the first priority to automate. After you are satisfied that no laws are being broken, and that you are not losing any money, the next area to look at is regressions: existing functionality. Nothing annoys a client (whether an actual end-customer or another service consuming your API) more, than having a feature that he used either break or outright dropped. Do not forget that when we are talking about APIs, most of your consumers are in fact going to be other developers. Change or loss of features for them means additional coding and testing on their end, because they still need to provide the same functionality to their customers as before.

API testing is an ongoing process that requires constant review and update.To ensure that your tests are still valid, relevant, and accurate, you need to review and update them regularly.Use a risk based approach to prioritize your tests and focus on the most critical areas. Automate your tests as much as possible to save time and improve consistency. Use a mix of unit, integration, and end-to-end tests to catch issues early. Keep your tests up-to-date by reviewing and updating them regularly. Use realistic test data to ensure accuracy.Monitor and maintain your tests to ensure that they are still working correctly.By following these best practices, you can ensure that your API tests are up-to-date, accurate, and relevant to your application

Regularly review and update your API tests to keep them aligned with changes in the application. As APIs evolve, ensure that your tests reflect the current functionality.

Understand the architecture and try to investigate about the API calls. If if they are internal or external API. Based on that educate the team and proceed further.

1) Use feature flags to test in production-like environments 2) Implement robust test data management with synthetic generation 3) Validate edge cases thoroughly, not just happy paths 4) Collaborate with other teams like docs, support, product 5) Trigger automated regression suites via CI/CD Leveraging automation, test data strategies, collaboration, and CI/CD integration are key to taking API testing to the next level. There are many facets, but those approaches help increase coverage, stability and speed.

API testing should be prioritized in a systematic and risk-based manner. First, identify important APIs such as user authentication, data retrieval, and core business logic. Consider API dependencies to spot potential problems. Test frequently used APIs for scalability and performance. Security is critical for APIs that handle sensitive data or pose a significant risk. Prioritise APIs that correspond with corporate goals and give a better user experience. This strategic approach provides efficient and successful API testing that aligns with the overall aims of your software development project.

I don't know what the article intends to articulate. All of these points are applicable to any standard testing type or area. Categorisation of test cases as per Risk, automation strategy and the testing pyramid is nothing new and common approach across any type of testing. Prioritisation of APIs can be based on business logic, the criticality of the business flow, the volume of traffic (load), data security and interface with the external world.

When working with APIs it is important to check the loads on APIs as well as server. Always consider the architecture you're following and its vulnerability to outside world such as prevention of DDoS attack. To avoid the load on APIs, please consider Rate Limiting, also you can use Load Balancers and you can monitor the Traffic on your APIs and consecutively use distribution to avoid breakage of your application/system.