Survey analyzes defect prediction and bug management in software repositories, highlighting AI and machine learning implications.
Introduction/ Importance of Study: Bug repository mining is a crucial research area in software engineering, analyzing software change trends, defect prediction, and evolution. It involves developing methods and tools for mining repositories, and providing essential data for bug management.Objective: The goal of this study is to analyze and synthesize recent trends in mining software bug repositories, providing valuable insights for future research and practical bug management.Noveltystatement:Our research contributes novel insights into mining software repositorytechniques and approaches employed in specific tasks such as bug localization, triaging, and prediction, along with their limitationsand possible future trends.Material and Method:This study presents a comprehensive survey that categorizes and synthesizes the current research within this field. This categorization is derived from an in-depth review of studies conducted over the past fifteen years, from 2010 to 2024. The survey is organized around three key dimensions: the test systems employed in bug repositories, the methodologies commonly used in this area of research, and the prevailing trends shaping the field.Results and Discussion: Our results highlightedthe significance of artificial intelligence and machine learning integration in bug repository mining; whichhas revolutionized the software development process by enhancing the classification, prediction,and vulnerability detection of bugs. Concluding Remarks: This survey aims to provide a clear and detailed understanding of the evolution of bug repository mining, offering valuable insights for the ongoing advancement of software engineering.
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Fatima et al. (2024) studied this question.
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