Predicting Expert Evaluations in Software Code Reviews
This paper explores the use of an algorithmic model to predict expert evaluations of software code commits, focusing on aspects often overlooked in manual code reviews due to time constraints.
3 min read
This paper explores the use of an algorithmic model to predict expert evaluations of software code commits, focusing on aspects often overlooked in manual code reviews due to time constraints. The research aims to augment, not replace, human review by providing automated insights into areas like coding time, implementation time, and code complexity.
Introduction
The research aims to augment, not replace, human review by providing automated insights into areas like coding time, implementation time, and code complexity.
Methodology
A random forest model was trained on evaluations from ten Java experts across 70 code commits, covering aspects such as code structure, quality metrics, implementation details, and architectural elements. The model uses static code analysis tied to Git commits.
Results
The model demonstrated strong correlations with human judgment, particularly for coding time (r=0.82) and implementation time (r=0.86). This suggests potential for accurate, automated effort estimation. While effective for time-based metrics, the model's performance was less robust for subjective qualities like code maintainability and author experience, highlighting the challenge of automating these nuanced assessments. This was likely influenced by lower inter-rater agreement on these subjective aspects.
Discussion
The model's efficiency (sub-second processing time per commit) offers significant time savings compared to manual reviews, allowing human reviewers to focus on higher-level aspects.
Conclusion
This research indicates that algorithmic models can effectively supplement manual code reviews by providing rapid and objective assessments of certain code characteristics, especially those related to development effort. This approach allows human reviewers to concentrate on more complex and subjective aspects of code quality, ultimately leading to a more efficient and comprehensive review process. Further research is needed to improve the model's handling of subjective qualities and extend its applicability to other programming languages.
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