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


di Biase, Marco; Bruntink, Magiel; van Deursen, Arie; Bacchelli, Alberto (2020). The effects of change decomposition on code review—a controlled experiment. PeerJ Computer Science, 5:193.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > General Computer Science
Language:English
Date:2020
Deposited On:26 Jan 2021 17:12
Last Modified:27 Jan 2021 21:01
Publisher:PeerJ, Ltd.
ISSN:2376-5992
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.7717/peerj-cs.193
Related URLs:https://peerj.com/articles/cs-193/
Other Identification Number:merlin-id:20240

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