Code Broker: A Multi-Agent System for Automated Code Quality Assessment
In a groundbreaking development in the realm of automated software quality assessment, researchers have introduced Code Broker, a sophisticated multi-agent system designed to evaluate Python code across various sources, including local files and GitHub repositories. This innovative tool leverages the Google Agent Development Kit (ADK) to generate comprehensive quality assessment reports that aim to enhance the overall quality of codebases.
System Architecture
Code Broker operates through a hierarchical architecture comprising five distinct agents. At the top of this structure is the root orchestrator, which manages a sequential pipeline agent. This agent is responsible for delegating tasks to three specialized agents that operate in parallel:
- Correctness Assessor: Evaluates the accuracy and correctness of the code.
- Style Assessor: Analyzes the coding style and adherence to best practices.
- Description Generator: Produces detailed descriptions and documentation for the code.
After the specialized agents complete their evaluations, an Improvement Recommender synthesizes the findings to create a holistic quality assessment report.
Quality Assessment Dimensions
The reports generated by Code Broker score four key dimensions of code quality:
- Correctness: Measures if the code functions as intended without errors.
- Security: Assesses vulnerabilities and risks associated with the code.
- Style: Evaluates adherence to coding standards and practices.
- Maintainability: Analyzes how easily the code can be modified or extended in the future.
The output reports are versatile, available in both Markdown and HTML formats, catering to the preferences of developers and teams.
Innovative Features
One of the standout features of Code Broker is its combination of large language model (LLM) based reasoning with deterministic static analysis signals sourced from Pylint. This dual approach enhances the robustness of the assessments. Additionally, the system employs asynchronous execution with retry logic, ensuring reliability even in complex scenarios.
Code Broker also explores lightweight session memory, allowing it to retain and query prior assessment contexts. This feature facilitates a more nuanced evaluation process, enabling the system to build on previous analyses for improved accuracy over time.
Preliminary Evaluation and Future Directions
The paper detailing Code Broker positions itself as a technical report focusing on system design and the orchestration of prompts and tools. A preliminary qualitative evaluation was conducted using representative Python codebases. Results indicated that the parallel specialized agents delivered readable, developer-oriented feedback, yet the evaluation uncovered certain limitations. Key areas needing improvement include:
- Depth of evaluation, especially in complex codebases.
- Enhancement of security tooling.
- Handling of large repositories more efficiently.
- Transitioning from in-memory persistence to more robust storage solutions.
For those interested in exploring Code Broker further, all code and reproducibility materials are publicly available on GitHub at https://github.com/Samir-atra/agents_intensive_dev.
As the demand for high-quality software continues to grow, tools like Code Broker represent a significant advancement in the automated assessment landscape, promising to empower developers with actionable insights to improve their codebases effectively.
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