{"ID":2827333,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16066","arxiv_id":"2512.16066","title":"Cold-Start Anti-Patterns and Refactorings in Serverless Systems: An Empirical Study","abstract":"Serverless computing simplifies deployment and scaling, yet cold-start latency remains a major performance bottleneck. Unlike prior work that treats mitigation as a black-box optimization, we study cold starts as a developer-visible design problem. From 81 adjudicated issue reports across open-source serverless systems, we derive taxonomies of initialization anti-patterns, remediation strategies, and diagnostic challenges spanning design, packaging, and runtime layers. Building on these insights, we introduce SCABENCH, a reproducible benchmark, and INITSCOPE, a lightweight analysis framework linking what code is loaded with what is executed. On SCABENCH, INITSCOPE improved localization accuracy by up to 40% and reduced diagnostic effort by 64% compared with prior tools, while a developer study showed higher task accuracy and faster diagnosis. Together, these results advance evidence-driven, performance-aware practices for cold-start mitigation in serverless design. Availability: The research artifact is publicly accessible for future studies and improvements.","short_abstract":"Serverless computing simplifies deployment and scaling, yet cold-start latency remains a major performance bottleneck. Unlike prior work that treats mitigation as a black-box optimization, we study cold starts as a developer-visible design problem. From 81 adjudicated issue reports across open-source serverless systems...","url_abs":"https://arxiv.org/abs/2512.16066","url_pdf":"https://arxiv.org/pdf/2512.16066v1","authors":"[\"Syed Salauddin Mohammad Tariq\",\"Foyzul Hassan\",\"Amiangshu Bosu\",\"Probir Roy\"]","published":"2025-12-18T01:20:41Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}