If At First You Don’t Succeed, Try, Try, Again...? Insights and LLM-informed Tooling for Detecting Retry Bugs in Software Systems

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Abstract
Retry---the re-execution of a task on failure---is a common mechanism to enable resilient software systems. Yet, despite its commonality and long history, retry remains difficult to implement and test. Guided by our study of real-world retry issues, we propose a novel suite of static and dynamic techniques to detect retry problems in software. We find that the ad-hoc nature of retry implementation poses challenges for traditional program analysis but can be well suited for large language models; and that carefully repurposing existing unit tests can, along with fault injection, expose various types of retry problems.
Funding Information
  • National Science Foundation (NS-2313190, CCF-2119184, CNS-1956180)
  • University of Chicago and Argonne National Laboratory (The Chameleon Cloud Project)
  • University of Chicago (Eckhardt Fellowship, Quad Undergraduate Research Grant)