מרק לסט

We investigate the use of data mining for the analysis of software metric databases, and some of the issues in this application domain. Software metrics are collected at various phases of the software development process, in order to monitor and control the quality of a software product. However, software quality control is complicated by the complex relationship between these metrics and the attributes of a software development process. Data mining has been proposed as a potential technology for supporting and enhancing our understanding of software metrics and their relationship to software quality. In this paper, we use fuzzy clustering to investigate three datasets of software metrics, along with the larger issue of whether supervised or unsupervised learning is more appropriate for software engineering problems. While our findings generally confirm the known linear relationship between metrics and change rates, some interesting behaviors are noted. In addition, our results partly contradict earlier studies that only used correlation analysis to investigate these datasets. These results illustrate how intelligent technologies can augment traditional statistical inference in software quality control.