Usage of Machine Learning in Software Testing

Abstract

Finding, pinpointing and correcting bugs in the software takes a lot of effort for software developers. Traditional testing requires humans to search and analyze data. Humans being prone to poor assumptions and hence skewed results leads to overlooked bugs. Machine learning helps systems to learn and apply the learned knowledge in the future which helps software testers with more accurate knowledge. Capability of several advanced machine learning techniques such as deep learning in a number of software engineering tasks such as code completion, defect prediction, bug localization, clone detection, code search and learning API sequences. A lot of approaches have been proposed by the researchers over the years at modifying programs automatically. Repairing programs by generate-and-validate techniques gain a lot by producing acceptable patches to the programmers and not overfitting through learning from past history and generating patches from correct code via probabilistic model. These approaches given the right environments play significant role in reducing the effort and time consumption as well as cost of the bug fixing for the software developers. In this chapter, various machine learning algorithms and their impact in software testing and bug fixing are explored. The last chapter concludes with the future of machine learning and predictive analysis and how they might be used for addressing the challenge of reacting faster to dynamic expectations of customers and their needs.