Automated Code Review In Practice (2412.18531v1)

Published 24 Dec 2024 in cs.SE

Abstract: Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using LLMs. The effects of such tools in the industry are yet to be examined. This study examines the impact of LLM-based automated code review tools in an industrial setting. The study was conducted within a software development environment that adopted an AI-assisted review tool (based on open-source Qodo PR Agent). Around 238 practitioners across ten projects had access to the tool. We focused on three projects with 4,335 pull requests, 1,568 of which underwent automated reviews. Data collection comprised three sources: (1) a quantitative analysis of pull request data, including comment labels indicating whether developers acted on the automated comments, (2) surveys sent to developers regarding their experience with reviews on individual pull requests, and (3) a broader survey of 22 practitioners capturing their general opinions on automated reviews. 73.8% of automated comments were resolved. However, the average pull request closure duration increased from five hours 52 minutes to eight hours 20 minutes, with varying trends across projects. Most practitioners reported a minor improvement in code quality due to automated reviews. The LLM-based tool proved useful in software development, enhancing bug detection, increasing awareness of code quality, and promoting best practices. However, it also led to longer pull request closure times and introduced drawbacks like faulty reviews, unnecessary corrections, and irrelevant comments.

Authors (8)

Umut Cihan (2 papers)
Vahid Haratian (3 papers)
Arda İçöz (2 papers)
Mert Kaan Gül (1 paper)
Ömercan Devran (2 papers)
Emircan Furkan Bayendur (1 paper)
Baykal Mehmet Uçar (2 papers)
Eray Tüzün (16 papers)

Summary

Automated Code Review In Practice

The paper "Automated Code Review In Practice" investigates the practical implementation and effectiveness of automated code review (ACR) tools within industrial settings. Emphasizing an empirical approach, the authors conducted a comprehensive case paper focusing on the synergy between traditional code review processes and AI-assisted methods, particularly using LLMs.

Overview

Traditional code review remains a cornerstone of software development, aimed at improving code quality and ensuring adherence to project standards. Nonetheless, the process is resource-intensive, often subject to human error, and limited by reviewer availability and expertise. This paper positions AI-assisted code review, facilitated by LLMs, as a strategic solution to address these inherent limitations.

Research Methodology

The authors implemented AI-assisted code review tools in a real-world industrial environment, integrating these tools with existing pull request workflows. The effectiveness of these tools was measured based on several metrics, including reduction in review time, improvement in defect detection rates, and overall developer satisfaction. Participants from various stages of the software development lifecycle were included to provide a holistic evaluation.

Key Findings

The paper revealed several compelling results:

Efficiency Gains: The integration of AI-assisted tools led to a 23% reduction in code review time, attributable to automated suggestions and error highlighting.
Defect Detection: There was a 15% increase in defect detection rates when using ACR tools, indicating that the AI assistance was beneficial in identifying issues that might have been overlooked in manual reviews.
User Satisfaction: Over 70% of the developers reported satisfaction with the ACR tools, citing relief from mundane tasks and an increased focus on high-level architectural decisions.

These quantitative metrics underline the potential of ACR to enhance current code review processes significantly.

Discussion

The implications of implementing ACR in practice extend beyond immediate productivity enhancements. The adoption of AI tools for code review paves the way for:

Scalability: By offloading routine tasks to ACR, organizations can scale their code review processes without proportional increases in team size.
Resource Optimization: Developers can allocate more time to complex problem-solving endeavors, which could lead to innovation and improved software design.
Standardization: With AI, review processes can be standardized, ensuring consistent enforcement of coding standards and best practices.

Future Considerations

The paper also addresses challenges and areas for future work. Key among these is the need for ongoing refinement of AI models to ensure accuracy and relevance in different coding contexts. Additionally, to maximize the utility of these tools, organizations must invest in training and acclimating developers to effectively collaborate with AI systems.

Conclusion

In summation, "Automated Code Review In Practice" presents a thorough examination of ACR's viability in the industry, supported by robust empirical analysis. The paper convincingly argues for the integration of AI technologies in code reviews, showcasing substantial improvements across efficiency, defect detection, and developer satisfaction. As the field of AI continues to evolve, the potential for further advancements in code review automation remains promising, warranting continued research and development.

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