Review highlights innovations in bug tracking systems and the influence of machine learning on software quality.
Managing software defects has always been a crucial challenge within the software development lifecycle. This paper reviews major advancements in bug tracking systems (BTS) by consolidating findings from recent research. Rather than merely describing these systems, it highlights three areas of innovation: the transition from generic to specialized bug management platforms, the use of context-aware algorithms for detecting duplicate reports, and the role of visualization techniques in handling continuous streams of bug data. The discussion also considers human factors and the growing influence of machine learning in automated triage and predictive analysis. The study concludes that BTS are evolving from static repositories into intelligent systems that reshape how bugs are reported, managed, and resolved, thereby improving efficiency and overall software quality.
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Pushparani.MK et al. (2025) studied this question.
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