The role of artificial intelligence in software testing: API tests

AI and its role in API testing

Using artificial intelligence in API testing, both during app development and maintenance, has many advantages; it will make your work faster and more efficient.

Artificial intelligence (in testing, programming and other domains) is based on so-called machine learning (ML), which involves algorithms and techniques that allow computers to “learn” from input data, sparing you the need to program them in detail.

AI software learns independently from available data. Since AI is a self-learning application, it follows that it should improve with time, or in our case, with the progress of software testing. After a while, it will be able to generate test cases, analyse test results and even adjust your testing strategy.

Of course, there are many ways you can use AI but, in terms of software testing – especially API testing – we will focus on the most important ones.

Test automation

Artificial intelligence can be used for automating API tests. Machine-learning algorithms can be run on test sets and used to identify API errors, anomalies and malfunctions.

Test case generation

Machine-learning algorithms can analyse the API specification, including available pathways, parameters, HTTP methods, etc., and then use such data to automatically generate test cases. This will allow you to create an extensive test set that will cover different parameter combinations and usage scenarios.

Test data generation

Artificial intelligence will also be great for generating different test data (including edge data) to better cover a range of test cases and detect potential errors and issues with data.

Test case optimisation

Letting artificial intelligence analyse your API test results will optimise you test case sets, eliminating redundancies and duplicates, so that you can focus on sensitive areas.

API response analysis

AI can analyse feedback from API, compare it with target results and report any discrepancies, errors and anomalies.

Performance analysis

The potential of AI can also be tapped for API performance monitoring and analysis, as well as load simulation. This is extremely helpful for detecting any potential bottlenecks and performance issues. These aspects are particularly important for high-traffic apps.

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Data analysis and visualisation

Machine-learning algorithms can monitor API behaviour in real time and detect anomalies in its feedback or response times. This can help you detect performance issues or errors that only occur under loads.

Test coverage analysis

AI systems can tell you which API elements have already been thoroughly tested and which have not, so you can identify gaps in test coverage and focus on elements that really need attention.

Anomaly detection

Machine-learning algorithms will also help you analyse API behaviour in real time and detect any anomalies.

Security analysis

By analysing potential attacks and anomalies, AI can help you detect app security vulnerabilities.

Test development support

Artificial intelligence can generate suggested test cases and scenarios, which makes it helpful in test development. With an ability to use real app test data, AI supports testers so they can identify more errors and problems.

AI can also help you generate test cases that might be difficult for the human mind to predict. This will increase your test coverage and detect errors more effectively.

Risk prediction

AI can analyse test and error histories and identify areas that have been particularly prone to issues. This will allow you to focus more attention on areas that require it and improve your risk management.

Automated test adjustment

Artificial intelligence can learn from test results and adapt to changes in the code or the API, automatically modifying relevant test cases in response to new functionalities or changes.

Automated document verification

Artificial intelligence can be used to support API document verification: AI compares it to actual feedback to detect any potential discrepancies.

CI/CD testing

AI can be used for automated CI (continuous integration) and CD (continuous delivery) testing to make sure new changes have not created any unexpected problems.

Regression tests

AI allows you to automate regression testing to sure code changes have not negatively affected existing API functions.

Will AI replace humans in all areas of software testing?

The examples discussed above definitely show that artificial intelligence has a wide range of diverse applications in API testing and software testing more generally. On the other hand, it cannot do without human supervision.

Testers still need to step in with their thorough understanding of API features and app business requirements to properly adapt algorithms to the context at hand. Based on coverage and result analysis, tests must be regularly recalibrated and enhanced. 

Context-sensitive tests, such as security tests or non-standard scenarios, should still be performed manually. Artificial intelligence will not do the testers’ job for them, but it can be a valuable intellectual and technical aid. 

There is no doubt that AI will be very useful in API testing, but it requires proper management, verification and contextualisation. It can be an excellent complement to traditional REST API testing.

Put simply, the future of artificial intelligence in software testing lies in improving the effectiveness and accuracy of human testers and developers.

Software testing: should I trust people or AI?

As you can see, AI-based software testing is an excellent way to save money and resources. But remember that, at least for now, AI can only support and complement traditional testing methods. 

At present, the ideal approach would be to combine both techniques. Artificial intelligence can be used to quickly test basic software functionalities, automatically go through repetitive test cases and deliver feedback on system stability.

However, more advanced tests, such as exploratory tests, performance tests or user experience evaluation should still be done by people. 

As AI technology evolves, its role in software testing is likely to grow, but it will still require human supervision, quality assurance and business savvy to deliver a high-quality software product. 

Artificial intelligence in IT products

At Altkom Software, we strive to deliver the best-quality products, so we never shy away from new technologies. Our testers rely on the support of artificial intelligence in their daily tasks to increase test quality and coverage. AI helps us analyse huge volumes of data generated during software tests, improving our detection of trends, patterns and issues.

Our testers use these analytics to optimise testing strategies and enhance software quality. 

Importantly, by improving test quality and accuracy, artificial intelligence helps us reduce the risk of errors and the costs that their elimination would entail at a later stage of the project.

Doesn’t that sound good? If you want your own projects to be tested by experienced testers supported by AI tools, contact us today!