Application of Data Mining Technology in Software Engineering

Abstract

Abstract With the rapid development of informatization, computer database software systems have entered various fields of society, which has brought about the explosive growth of industry data. Faced with massive amounts of data, computers with limited storage capacity have to abandon some outdated data, and the application of various data mining technologies related to it has gradually matured. The purpose of this article is to discuss the application research of data mining technology in software engineering. This article analyzes the correlation analysis of a large number of bug repair source code update data and bug defect reports in the version control system SVN and the defect tracking system Bugzilla in the software engineering project development process, and tries to classify the bug report by data mining technology: defect changes and potential defects change. Starting from large-scale software engineering projects, apply data mining technology to the huge software engineeri ng knowledge base. Especially the software development and maintenance are explained, as well as the more challenging problems in the future. This paper uses data mining technology to study the dependency of the source code files of each module of the software system, and helps software developers quickly understand the software architecture by understanding the interrelationships between the modules, and provides suggestions for modification paths. Experimental research shows that this paper compares with F-measure and concludes that FL-M-GSpan algorithm is better than TS-M-GSpan algorithm. At the same time, it is found that the FL-M-GSpan algorithm always has a better accuracy rate close to 95%, while the TS-M-GSpan algorithm always has a better recall rate.