The effects of change decomposition on code review -- a controlled experiment

TL;DR

This study quantitatively evaluates how decomposing code changes into smaller, coherent parts affects the efficiency and approach of code review, showing it reduces errors and aids reviewer focus without affecting defect detection.

Contribution

It provides the first quantitative evidence that change decomposition improves review quality and reviewer behavior, supporting best practices in software engineering.

Findings

Fewer wrongly reported issues with decomposed changes

Increased context-seeking behavior during review

No significant difference in defect detection or understanding

Abstract

Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis. Aims: (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes. Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students. Results: Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects. Conclusions: Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering.