Artificial Intelligence Methods in Software Testing

Student: Toropova Lidiya

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Data Mining (Master)

Year of Graduation: 2017

Modern approaches to software development are often iterative. Design, coding, testing are often combined, or alternate cyclically with a new release cycle. The testing process can be carried out continuously, because the source code is constantly changing. The greater the amount of source code, the more effort is needed to maintain the quality of such a project at good level. In order to find defects earlier, defect prediction models can be used which, based on various metrics (source code or processes) of previous releases, detect modules in which the probability of containing defects is large. This allows developers to detect problems earlier, and testers prioritize tests. In this paper, algorithms for building defect prediction models are observed and a new approach is proposed for building defect prediction model for new projects that do not have enough data of previous releases.

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