Application of machine learning in software engineering: an overview

• Published in: PROBLEMS IN PROGRAMMING
• Date: 2019
• Issue: 4
• Pages: 92-110
• ISSN: 1727-4907
• Author Affiliations: O.H. Moroz — International Scientific and Educational Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and the Ministry of Education and Science of Ukraine; H.B. Moroz — Institute of Software Systems NAS of Ukraine
• Main Authors: Moroz, O.H., Moroz, H.B.
• Format: Article
• Language: Russian
• Published: PROBLEMS IN PROGRAMMING 2019
• Subjects: software engineering; software; machine learning; neural networks; support vector machine; decision trees; UDC 004.4(075.8)
• Online Access: https://pp.isofts.kiev.ua/index.php/ojs1/article/view/381
• Journal Title: Problems in programming

Today, software is one of the main technologies contributing to the development of society. Therefore, its quality is a major requirement for both the global software industry and software engineering, which deals with all aspects of improving the quality and reliability of software products at all stages of their life cycle. To solve software engineering problems, the use of artificial intelligence methods is becoming increasingly relevant. The article presents a brief description of machine learning methods such as artificial neural networks, support vector machine, decision trees, inductive logic programming and others. Also, examples of the application of these methods to solve some problems of forecasting and quality assessment in software engineering are presented, recommendations for applying machine learning algorithms to solving problems of software engineering are given. The review will be useful by researchers and practitioners as a starting point, because it identifies important and promising areas of research. This will ultimately lead to more effective solving of software engineering problems, providing better, more reliable and cost effective software products.Problems in programming 2019; 4: 92-110