Cold-Start Anti-Patterns and Refactorings in Serverless Systems: An Empirical Study

TL;DR

This paper investigates cold-start latency in serverless systems as a design problem, analyzing issues, proposing benchmarks and tools, and demonstrating improved diagnosis and developer performance.

Contribution

It introduces a taxonomy of cold-start anti-patterns, the SCABENCH benchmark, and INITSCOPE analysis framework, advancing practical solutions for cold-start mitigation.

Findings

INITSCOPE improved localization accuracy by up to 40%.

Diagnostic effort was reduced by 64%.

Developers showed higher task accuracy and faster diagnosis.

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.