Matt Pope, Jonathan Sillito · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.27380 · cited by 0 works (OpenAlex) · open-access PDF
최종 도구 움직임: 소프트웨어 사건이 발생한 후, 조직은 소프트웨어 시스템의 신뢰성을 개선하려고 시도하여 사건 후 분석을 수행하고 유사한 미래 사건을 예방하거나 완화하기위한 행동을 식별합니다.이 활동의 중요성에도 불구하고, 소프트웨어 진화의 성격과 특성은 분석 과정에 의해 유발되는 방법에 대한 제한된 이해가 있습니다. METHOD: 이 연구는 10 년간의 장거리 데이터 세트의 혼합 방법 분석을 제공함으로써 이러한 문제를 개선하려고합니다. 우리는 이러한 변화가 어떻게 나타나는지에 대한 이해를 구축하는 것을 목표로하고, 특히 그 특성 (작동, 위치 및 타이밍 등)이 표준 유지 보수 및 진화 변화와 어떻게 다릅니다. METHOD: 우리는 10 년간의 장거리 데이터
Machine translation (Meta M2M100); the English original follows.
BACKGROUND: After a software incident, organizations attempt to improve the reliability of their software systems by conducting post-incident analysis and identifying actions to prevent or mitigate similar future incidents. Despite the importance of this activity, there is a limited understanding of the nature and characteristics of the software evolution triggered by the analysis process. AIMS: This study seeks to bridge this gap by providing the first empirical characterization of post-incident software evolution. We aim to establish an understanding of how these changes manifest, specifically focusing on how their characteristics (such as effort, location, and timing) differ from standard maintenance and evolutionary changes. METHOD: We conducted a mixed-methods analysis of a 10-year longitudinal dataset from Wikimedia, examining 1,302 tasks and 2,203 code reviews. We categorized these changes across five dimensions: target, effort, abandonment rate, prioritization, and time-to-resolve. RESULTS: Our results show that the post-incident evolution is distinct from other change types. Post-incident changes are more time-consuming, with a median completion time over twice that of other changes and are disproportionately concentrated in a server configuration repository. CONCLUSION: By identifying a few of the characteristics of post-incident evolution, this work provides a foundation for future research into reliability engineering and learning from failure. We highlight further avenues for exploration necessary to fully understand post-incident evolution. Lastly, our work enables researchers to move beyond high-level analysis and begin developing specialized methods and tooling tailored to the nature of post-incident evolution.
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Topics: Software evolution, Computer science, Software engineering, Software, Reliability (semiconductor), Abandonment (legal)
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