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.

It is well known that valuable information is contained in the natural language components of software systems, like comments and manual, and such information can be used to improve system performance and reliability. Past research has attempted to extract such information through task-specific machine learning models and tool chains. Here, we investigate a general, one-model-fit-all solution through a state-of-the-art large language model (e.g., the GPT series). Our investigation covers three representative tasks: extracting locking rules from comments, synthesizing exception predicates from comments, and identifying performance-related configurations; it reveals challenges and opportunities in applying large language models to system maintenance tasks.