Unraveling the Influences on Bug Fixing Time: A Comparative Analysis of Causal Inference Model
Authors: 
Sien Reeve Ordonez Peralta, Hironori Washizaki, Yoshiaki Fukazawa, Yuki Noyori, Shuhei Nojiri, Hideyuki KanukaAuthors Info & Claims
Sien Reeve Ordonez Peralta, Hironori Washizaki, Yoshiaki Fukazawa, Yuki Noyori, Shuhei Nojiri, Hideyuki KanukaAuthors Info & ClaimsPages 393 - 398
Abstract
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.
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June 2024
728 pages
ISBN:9798400717017
DOI:10.1145/3661167
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 18 June 2024
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EASE 2024: 28th International Conference on Evaluation and Assessment in Software Engineering
June 18 - 21, 2024
Salerno, Italy
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