Using AI and Machine Learning in QA Testing
Keywords:
Artificial Intelligence, Machine Learning, QA Testing, Automation, Defect Prediction, Anomaly AnalysisAbstract
The article will consider the possibilities of using artificial intelligence (AI) and machine learning (ML) technologies in the field of software quality control due to the fact that they are able to change the usual approaches to testing due to their abilities. Methods of using AI and ML to increase the effectiveness of quality assurance (QA) will be considered: automation of tests, detection of defects, prediction of anomalies. The methodology is based on the analysis of scientific papers, which will describe achievements in the application of these technologies during QA, including adaptive algorithms that automatically generate tests, clustering methods that systematize errors, and big data analysis that allows predicting defects. As part of the work, examples of organizations that demonstrate comparing user interface testing using a manual method and automated regression tests will also be considered. The data obtained show a decrease in the time spent on testing, a decrease in the probability of missing errors, and an improvement in the quality of processes. The information in the work will be useful to quality specialists, developers, and AI researchers working on optimizing testing. In conclusion, the article notes the success of applying such technological solutions in achieving QA goals.
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