Resource leaks, where resources like files, database connections, or streams aren’t properly released after use, are a persistent issue in Java applications. These leaks can lead to performance degradation, and system failures. While tools like SonarSource SonarQubeTM effectively identify such leaks, the fixing process remains manual, time-consuming, and prone to errors. To address this, we developed FixrLeak, a generative AI-based framework that automates the detection and repair of resource leaks. FixrLeak combines Abstract Syntax Tree (AST) analysis with generative AI (GenAI) to produce accurate, idiomatic fixes while following Java best practices like try-with-resources. Deployed within Uber’s extensive Java codebase, FixrLeak significantly reduces manual effort, improves developer productivity, and improves code quality, showcasing the transformative potential of AI-driven solutions in large-scale software engineering.

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Resource leaks in Java applications can lead to performance issues and system failures. FixrLeak is a generative AI-based framework developed by Uber that automates the detection and repair of resource leaks using Abstract Syntax Tree analysis. This approach, implemented within Uber's Java codebase, reduces manual effort and improves code quality by efficiently fixing leaks. FixrLeak focuses on easily manageable leaks and has demonstrated success in fixing 93 out of 102 analyzed cases. Future improvements aim to extend its capabilities to handle more complex scenarios and other programming languages like Golang.

Fixrleak: Fixing Java Resource Leaks with GenAI