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Characterizing Post-Incident Software Evolution

Matt Pope, Jonathan Sillito · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27380 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Setelah insiden perangkat lunak, organisasi mencoba untuk meningkatkan keandalan sistem perangkat lunak mereka dengan melakukan analisis pasca insiden dan mengidentifikasi tindakan untuk mencegah atau mengurangi insiden masa depan yang serupa. Terlepas dari pentingnya kegiatan ini, ada pemahaman yang terbatas tentang sifat dan karakteristik evolusi perangkat lunak yang dipicu oleh proses analisis. METHOD: Studi ini bertujuan untuk membongkar kesenjangan ini dengan menyediakan karakterisasi empiris pertama dari evolusi perangkat lunak pasca insiden. Kami bertujuan untuk membangun pemahaman tentang bagaimana perubahan ini dimanifestasikan, secara khusus berfokus pada bagaimana karakteristik mereka (seperti upaya, lokasi, dan waktu) berbeda dari pemeliharaan standar dan perubahan evolusi. METHOD: Kami melakukan analisis metode campuran dari set data longitudinal 10 tahun, mengeksplorasi 1.302 tugas Wikimedia dan 2.203

Machine translation (Meta M2M100); the English original follows.

Abstract

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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