Islem Bouzenia

Pascal Joos, Islem Bouzenia, Michael Pradel

Static analysis tools are widely used to detect bugs, vulnerabilities, and code smells. Traditionally, developers must resolve these warnings manually. Because this process is tedious, developers sometimes ignore warnings, leading to an accumulation of warnings and a degradation of code quality. This paper presents CodeCureAgent, an approach that harnesses LLM-based agents to automatically analyze, classify, and repair static analysis warnings. Unlike previous work, our method does not follow a predetermined algorithm. Instead, we adopt an agentic framework that iteratively invokes tools to gather additional information from the codebase (e.g., via code search) and edit the codebase to resolve the warning. CodeCureAgent detects and suppresses false positives, while fixing true positives when identified. We equip CodeCureAgent with a three-step heuristic to approve patches: (1) build the project, (2) verify that the warning disappears without introducing new warnings, and (3) run the test suite. We evaluate CodeCureAgent on a dataset of 1,000 SonarQube warnings found in 106 Java projects and covering 291 distinct rules. Our approach produces plausible fixes for 96.8% of the warnings, outperforming state-of-the-art baseline approaches by 29.2%-34.0% in plausible-fix rate. Manual inspection of 291 cases reveals a correct-fix rate of 86.3%, showing that CodeCureAgent can reliably repair static analysis warnings. The approach incurs LLM costs of about 2.9 cents (USD) and an end-to-end processing time of about four minutes per warning. We envision CodeCureAgent helping to clean existing codebases and being integrated into CI/CD pipelines to prevent the accumulation of static analysis warnings.

Islem Bouzenia, Michael Pradel

The ability to execute the test suite of a project is essential in many scenarios, e.g., to assess code quality and code coverage, to validate code changes made by developers or automated tools, and to ensure compatibility with dependencies. Despite its importance, executing the test suite of a project can be challenging in practice because different projects use different programming languages, software ecosystems, build systems, testing frameworks, and other tools. These challenges make it difficult to create a reliable, universal test execution method that works across different projects. This paper presents ExecutionAgent, an automated technique that prepares scripts for building an arbitrary project from source code and running its test cases. Inspired by the way a human developer would address this task, our approach is a large language model (LLM)-based agent that autonomously executes commands and interacts with the host system. The agent uses meta-prompting to gather guidelines on the latest technologies related to the given project, and it iteratively refines its process based on feedback from the previous steps. Our evaluation applies ExecutionAgent to 50 open-source projects that use 14 different programming languages and many different build and testing tools. The approach successfully executes the test suites of 33/50 projects, while matching the test results of ground truth test suite executions with a deviation of only 7.5%. These results improve over the best previously available technique by 6.6x. The costs imposed by the approach are reasonable, with an execution time of 74 minutes and LLM costs of USD 0.16, on average per project. We envision ExecutionAgent to serve as a valuable tool for developers, automated programming tools, and researchers that need to execute tests across a wide variety of projects.