Applications of Data Mining in Software Engineering: Techniques, Trends, and Case Studies

Abstract

Data mining, the process of discovering patterns and knowledge from large quantities of data, has come a vital tool in software engineering, transubstantiating colorful aspects of the field through its advanced logical capabilities. This paper explores the different operations of data mining ways in software engineering, pressing their impact on perfecting software quality, effectiveness, and operation. We give a comprehensive overview of crucial data mining styles, including bracket, clustering, association rule mining, anomaly discovery, and textbook mining, and their specific operations within software disfigurement vaticination, quality assurance, conservation, design operation, and security.

          Through an analysis of contemporary case studies and empirical data, this paper illustrates how data mining ways have been effectively employed to prognosticate disfigurement-prone areas in software, optimize testing processes, and enhance design estimation and monitoring. Also, we address the challenges and limitations encountered in enforcing data mining results and bandy arising trends similar as the integration of data mining with machine literacy and artificial intelligence.