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What happens in my code reviews? An investigation on automatically classifying review changes


Fregnan, Enrico; Petrulio, Fernando; Di Geronimo, Linda; Bacchelli, Alberto (2022). What happens in my code reviews? An investigation on automatically classifying review changes. Empirical Software Engineering, 27(4):89:1-89:43.

Abstract

Code reviewing is a widespread practice used by software engineers to maintain high code quality. To date, the knowledge on the effect of code review on source code is still limited. Some studies have addressed this problem by classifying the types of changes that take place during the review process (a.k.a. review changes), as this strategy can, for example, pinpoint the immediate effect of reviews on code. Nevertheless, this classification (1) is not scalable, as it was conducted manually, and (2) was not assessed in terms of how meaningful the provided information is for practitioners. This paper aims at addressing these limitations: First, we investigate to what extent a machine learning-based technique can automatically classify review changes. Then, we evaluate the relevance of information on review change types and its potential usefulness, by conducting (1) semi-structured interviews with 12 developers and (2) a qualitative study with 17 developers, who are asked to assess reports on the review changes of their project. Key results of the study show that not only it is possible to automatically classify code review changes, but this information is also perceived by practitioners as valuable to improve the code review process.

Abstract

Code reviewing is a widespread practice used by software engineers to maintain high code quality. To date, the knowledge on the effect of code review on source code is still limited. Some studies have addressed this problem by classifying the types of changes that take place during the review process (a.k.a. review changes), as this strategy can, for example, pinpoint the immediate effect of reviews on code. Nevertheless, this classification (1) is not scalable, as it was conducted manually, and (2) was not assessed in terms of how meaningful the provided information is for practitioners. This paper aims at addressing these limitations: First, we investigate to what extent a machine learning-based technique can automatically classify review changes. Then, we evaluate the relevance of information on review change types and its potential usefulness, by conducting (1) semi-structured interviews with 12 developers and (2) a qualitative study with 17 developers, who are asked to assess reports on the review changes of their project. Key results of the study show that not only it is possible to automatically classify code review changes, but this information is also perceived by practitioners as valuable to improve the code review process.

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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 > Software
Scope:Discipline-based scholarship (basic research)
Language:English
Date:14 April 2022
Deposited On:09 Mar 2023 13:39
Last Modified:29 Apr 2024 01:36
Publisher:Springer
ISSN:1382-3256
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s10664-021-10075-5
Other Identification Number:merlin-id:23373
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)