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5 Application Areas of AI in Softwaretesting

Artificial Intelligence on the Rise

For several years now, artificial intelligence (AI) and machine learning (ML) have been considered trend-setting technologies. Reliable software for self-driving cars or further developments in robot technology would hardly be possible without AI and ML. It is therefore no surprise that leading global companies such as Tesla or Google are not only focusing on these technologies, but are also actively driving development forward. Especially since the ever-growing amount of data and information in all areas of our society makes the use of AI and ML indispensable. An efficient analysis and evaluation of this data would not be possible without AI and ML. However, significant added value from large volumes of data can only be achieved through targeted evaluation.

Software Quality Assurance must keep Pace

Not only large companies with almost unlimited resources have the possibility to use AI and ML to improve their software and products. Due to the enormously increased availability of computing power and memory in recent years, ML approaches have become easier to implement. This is one of the reasons why they have spread into more and more application areas. One of these areas is the quality assurance of software. In order to keep up with the development, QA must inevitably develop further and rely on new technologies. In fact, comprehensive software testing is more important than ever to eliminate critical defects in AI-based programs. This is particularly evident in the example of self-driving cars.

In Practice: Artificial Intelligence in QA

How exactly can we benefit from AI and ML in quality assurance, where can we apply these technologies? We have compiled five examples that illustrate how new paths are already being taken in QA with artificial intelligence:

1. AI in Code Analysis

ML can already be used in the early phase of development, e.g. to support code reviews. By applying the knowledge gained from analyzing code, best practices and common errorML can already be used in the early phase of development, e.g. to support code reviews. By applying the knowledge gained from analyzing code, best practices and common defect sources, even the code of large programs with complex functions can be automatically analyzed and searched for potential errors and vulnerabilities with much less manual effort. This not only saves time and effort, but also finds defects that are easy to overlook or only occur in specific constellations. Connections, dependencies and areas of particular risk are also detected. Good examples are DeepCode or Infer.

2. AI in the test case creation

Machine Learning can be very useful for test case creation, whether manual or automated. First of all, by analyzing areas of high risk or simple coverage analyses, the decision which test cases to create can be simplified. In addition, the existing test case catalog can be checked for redundant or ineffective test cases. Automated test case creation is also made easier with automatic scripting through interpretation of continuous text or even directly based on user behavior. Current applications that rely on AI for test case creation include Appvance IQ and Functionize.

3. AI during manual test execution

Manual test execution can also benefit greatly from artificial intelligence when analyzing the results of previous or current executions. Areas of high risk or high defect detection potential are thus identified at an early stage. Based on the results, the scope and focus of further test executions can be planned more precisely, making the entire test process much more efficient. Time savings with higher quality are usually the result. AI is used in this application at perfecto.

4. AI in automated test case execution

One of the largest application areas of AI and ML is image recognition. This can be used for automated test cases. It allows to create much more robust automated test cases that run stable even when changes are made. For example, certain areas of the software, such as the shopping cart, can be automatically recognized even if the appearance, placement or internal IDs have changed. In the same way, all other properties of the elements of a page, such as id, size, placement, tags, etc. can be weighted and evaluated to determine the elements during test execution. ML can then be used to reduce the weighting for attributes that change frequently. Thus, the test cases continue to run stable. Examples in this area are testcraft and applitools.

5. AI in the analysis of test results

The analysis of test results is also perfectly suited for ML algorithms. Often you have huge amounts of outcome data, huge log files, defect messages and screenshots that have to be examined in a time-consuming way. With ML the data can be analyzed and evaluated quickly and efficiently and the relevant information can be picked out. In the best case, defects can be generated automatically and core problems in the software can be identified. Also the search for the origin of the defects and possibly even the correction in the code can be learned and automatically implemented. This technique is used by loomsystems.

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