Volume 2 - Issue 4, July - August 2026

Software maintenance remains one of the most resource-intensive phases of the software development life cycle, frequently consuming 60 to 80 percent of the total lifetime cost of a software product. Although bug tracking systems accumulate vast repositories of historical defect data, most organizations continue to rely on reactive, experience-based maintenance strategies rather than systematic, data-driven approaches. This study, titled “Optimizing Software Maintenance by Predictive Analytics on Bug Tracking Data,” develops and empirically tests a conceptual model examining how the quality of bug tracking data, the extent of predictive analytics usage, and team expertise in analytics influence the effectiveness of software maintenance. Primary data were collected through a structured questionnaire administered to 200 software developers, testers, project managers, and data analysts associated with a software organization in Coimbatore, India, and analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple linear regression in SPSS. The results show that all three independent variables have a statistically significant, positive relationship with software maintenance effectiveness, jointly explaining 69.3 percent of the variance in the outcome. Predictive analytics usage emerged as the strongest predictor, followed by quality of bug tracking data and team expertise in analytics. The findings confirm that converting historical defect data into actionable intelligence requires not only reliable data and capable predictive tools but also the analytical competence of maintenance teams to interpret and apply model outputs. The study contributes an integrated, empirically validated framework linking data quality, analytics adoption, and human expertise to maintenance outcomes, and offers practical guidance for organizations seeking to transition from reactive defect handling toward proactive, predictive maintenance management.

Vijay S, Dr.N.Nirmala Devi,"Optimizing Software Maintenance by Predictive Analytics on Bug Tracking Data: A Study in India" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(152-159) July - August 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.