In this study, we employ causal inference models, specifically Bayesian Networks (BN) and Linear Non-Gaussian Acyclic Models (LiNGAM), to investigate the determinants of Bug Fixing Time (BFT) in software development. Moving beyond traditional statistical analyses, our approach aims to identify the true causal factors influencing BFT. Our findings indicate that ’Reporter Reputation’, ’Severity’, and ’Blocker’ status are significant determinants of BFT, with notable differences between bugs reported by users versus developers. This research challenges existing assumptions about the necessity of comprehensive bug reports and underscores the importance of understanding bug resolution’s complexity and organizational context. By applying causal inference models, we offer actionable insights for improving bug prioritization, operational efficiency, and predictive management of development bottlenecks, enhancing the software development lifecycle. Our study bridges the theoretical and practical aspects of software quality optimization and introduces a novel perspective on managing software development processes. Additionally, our analysis reveals counterintuitive results that further contribute to our understanding of the dynamics influencing BFT.

Most client software employs a bug-tracking system, which utilizes user-submitted reports (bug reports) that contain information necessary for software developers to fix bugs. The quality of bug reports drastically differs. Bug reports can include severity, priority, and associated issues determined by researching the addressed bug. Herein we investigate the influence of bug report qualities on successfully fixing a bug and estimating the fixing time. We also examine the claim in previous studies that bias and differences in the treatment of bug reports exist due to broad expertness among the reporters. Our approach examines the relationship between the qualities within the bug-fixing cycle and modeling graphical causal dependencies through a Bayesian Network. Bug reports with attachments, dependencies on another bug, and frequent discussions are more likely to be fixed. In addition, bug reports with a high severity tend to be fixed faster. Moreover, the difficulty of the bug itself may influence the fixing rate such that a straightforward bug will be fixed easier and faster regardless of the bug report quality.