The relationship between evolutionary coupling and defects in large industrial software. Issue 4 (7th February 2017)

• Record Type: Journal Article
• Authors: Kirbas, Serkan; Caglayan, Bora; Hall, Tracy; Counsell, Steve; Bowes, David; Sen, Alper; Bener, Ayse
• Is Part Of: Journal of software. Volume 29:Issue 4(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-07
• Subjects: evolutionary coupling -- industrial software -- legacy software -- mining software repositories -- measurement -- software defects; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1842
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Evolutionary coupling (EC) is defined as the implicit relationship between 2 or more software artifacts that are frequently changed together. Changing software is widely reported to be defect‐prone. In this study, we investigate the effect of EC on the defect proneness of large industrial software systems and explain why the effects vary. We analysed 2 large industrial systems: a legacy financial system and a modern telecommunications system. We collected historical data for 7 years from 5 different software repositories containing 176 thousand files. We applied correlation and regression analysis to explore the relationship between EC and software defects, and we analysed defect types, size, and process metrics to explain different effects of EC on defects through correlation. Our results indicate that there is generally a positive correlation between EC and defects, but the correlation strength varies. Evolutionary coupling is less likely to have a relationship to software defects for parts of the software with fewer files and where fewer developers contributed. Evolutionary coupling measures showed higher correlation with some types of defects (based on root causes) such as code implementation and acceptance criteria. Although EC measures may be useful to explain defects, the explanatory power of such measures depends on defect types, size, and process metrics.