Harnessing Machine Learning for Software Testing: A Comprehensive Approach

Introduction In recent years, machine learning has emerged as a powerful tool for solving complex problems, leading to its successful application in various fields. The software testing domain is no exception, and by leveraging machine learning, testers can significantly improve the efficiency and effectiveness of their testing efforts. In this article, we will explore how machine learning can be applied to various aspects of software testing, such as analyzing server logs, examining functional testing logs, scrutinizing defect titles and descriptions, evaluating performance testing logs, and assessing end-user reviews in app stores. Ultimately, this approach can help in clustering errors, predicting future defects, and enhancing app performance by identifying latency issues. Analyzing Server Logs Server logs are an essential source of information for testers, as they contain valuable data about system events, errors, and user behavior. By employing machine learning algorithms on serve