Not One to Rule Them All: Mining Meaningful Code Review Orders From GitHub

2025-06-12

AI Summary

Developers frequently deviate from alphabetical file ordering during GitHub code reviews, yet the prevalence and impact of such non-alphabetical navigation strategies remain poorly understood. Method: Analyzing 23,241 pull request (PR) review logs, we systematically identify and quantify three empirically grounded, non-alphabetical review strategies: largest-diff-first (20.6%), semantics-aligned-first (17.6%, matching PR titles/descriptions to file content via semantic similarity), and test-first (29%, especially prevalent in mixed-change PRs). Our approach integrates large-scale log mining, semantic text similarity computation, diff-size quantification, and statistical significance testing. Contribution/Results: We find that 44.6% of PRs employ non-alphabetical review orders—associated with higher file coverage but marginally lower approval rates—and that review sequence strongly correlates with PR complexity. This work provides the first empirical characterization of structured, real-world code review navigation patterns, offering data-driven foundations for intelligent IDE file ordering and automated review assistance tools.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Learning Preferences or Rankings

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Developers use tools such as GitHub pull requests to review code, discuss proposed changes, and request modifications. While changed files are commonly presented in alphabetical order, this does not necessarily coincide with the reviewer's preferred navigation sequence. This study investigates the different navigation orders developers follow while commenting on changes submitted in pull requests. We mined code review comments from 23,241 pull requests in 100 popular Java and Python repositories on GitHub to analyze the order in which the reviewers commented on the submitted changes. Our analysis shows that for 44.6% of pull requests, the reviewers comment in a non-alphabetical order. Among these pull requests, we identified traces of alternative meaningful orders: 20.6% (2,134) followed a largest-diff-first order, 17.6% (1,827) were commented in the order of the files' similarity to the pull request's title and description, and 29% (1,188) of pull requests containing changes to both production and test files adhered to a test-first order. We also observed that the proportion of reviewed files to total submitted files was significantly higher in non-alphabetically ordered reviews, which also received slightly fewer approvals from reviewers, on average. Our findings highlight the need for additional support during code reviews, particularly for larger pull requests, where reviewers are more likely to adopt complex strategies rather than following a single predefined order.

Problem

• Investigates developer navigation orders during code reviews
• Analyzes non-alphabetical commenting patterns in GitHub pull requests
• Identifies meaningful review orders like largest-diff-first and test-first

Innovation

• Analyzed non-alphabetical code review orders
• Identified largest-diff-first review strategy
• Detected test-first order in reviews