This research presents a novel approach to automating fault localization in software crashes using fine-tuned Large Language Models trained on mutation-generated stack traces.

• Addresses the challenge of production crashes where only stack traces are available
• Uses code mutations to generate synthetic training data for LLMs
• Demonstrates improved accuracy in identifying root causes of software failures
• Especially valuable for complex enterprise systems like SAP HANA

This innovation significantly reduces the manual effort needed by engineering teams to diagnose software crashes, potentially saving countless hours during incident response and improving system reliability.

Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces