The effects of change-decomposition on code review - A Controlled Experiment
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Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how changesets divided according to a logical partitioning could be easier to review. 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 support 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.
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