Partitioning Composite Code Changes to Facilitate Code Review

Tao, Yida; Kim, Sunghun

doi:10.1109/msr.2015.24

Cited by 45 publications

(37 citation statements)

References 26 publications

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“…Barnett et al (2015) investigated change untangling in the context of code review and obtained positive results in a user study. Tao and Kim (2015) also proposed to use change untangling for review and showed in a user study that untangling code changes can improve code understanding.…”

Section: Code Change Size/untangling In Code Reviewmentioning

confidence: 99%

“…1 Code review performance is traditionally measured as the share of defects found (effectiveness) and defects found per unit of time (efficiency) (Biffl, 2000). Past research (e.g., Porter et al (1998); Bacchelli and Bird (2013)) has provided evidence that code review performance is determined to a large degree by human factors, thus current research efforts focus on investigating how automated tooling can help humans performing reviews with higher efficiency and effectiveness (Baum and Schneider, 2016;Thongtanunam et al, 2015b;Balachandran, 2013;Tao and Kim, 2015;Barnett et al, 2015;Kalyan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Associating working memory capacity and code change ordering with code review performance

2019

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Change-based code review is a software quality assurance technique that is widely used in practice. Therefore, better understanding what influences performance in code review and finding ways to improve it can have a large impact. In this study, we examine the association of working memory capacity and cognitive load to code review performance and we test the predictions of a recent theory regarding improved code review efficiency with certain code change part orders. We perform a confirmatory experiment with 50 participants, mostly professional software developers. The participants performed code reviews on one small and two larger code changes from an open source software system to which we had seeded additional defects. We measured their efficiency and effectiveness in defect detection, their working memory capacity, and several potential confounding factors. We find that there is a moderate association between working memory capacity and the effectiveness of finding delocalized defects, influenced by other factors, whereas the association with other defect types is almost non-existing. We also confirm that the effectiveness of reviews is significantly larger for small code changes. We observe a tendency that the order of presenting the code changes influences the efficiency of code review, but cannot conclude this reliably.

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Section: Code Change Size/untangling In Code Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associating working memory capacity and code change ordering with code review performance

2019

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“…A line of research that also deals with the clustering of change parts is "change untangling" [47], [48]. A number of approaches have been proposed that cluster changes based on heuristics and pattern matching [9], [49], [50], [51]. Future work should check whether these approaches can be adjusted to help in finding an optimal tour.…”

Section: Clustering Of Program Fragments and Change Partsmentioning

confidence: 99%

On the Optimal Order of Reading Source Code Changes for Review

Baum

Schneider

Bacchelli

2017

2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Change-based code review, e.g., in the form of pull requests, is the dominant style of code review in practice. An important option to improve review's efficiency is cognitive support for the reviewer. Nevertheless, review tools present the change parts under review sorted in alphabetical order of file path, thus leaving the effort of understanding the construction, connections, and logic of the changes on the reviewer. This leads to the question: How should a code review tool order the parts of a code change to best support the reviewer? We answer this question with a middle-range theory, which we generated inductively in a mixed methods study, based on interviews, an online survey, and existing findings from related areas. Our results indicate that an optimal order is mainly an optimal grouping of the change parts by relatedness. We present our findings as a collection of principles and formalize them as a partial order relation among review orders. Abstract-Change-based code review, e.g., in the form of pull requests, is the dominant style of code review in practice. An important option to improve review's efficiency is cognitive support for the reviewer. Nevertheless, review tools present the change parts under review sorted in alphabetical order of file path, thus leaving the effort of understanding the construction, connections, and logic of the changes on the reviewer. This leads to the question: How should a code review tool order the parts of a code change to best support the reviewer? We answer this question with a middle-range theory, which we generated inductively in a mixed methods study, based on interviews, an online survey, and existing findings from related areas. Our results indicate that an optimal order is mainly an optimal grouping of the change parts by relatedness. We present our findings as a collection of principles and formalize them as a partial order relation among review orders.

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“…According to their results, this would help time to review as the different contexts are separated. Tao & Kim (2015) proposed a heuristic-based approach to decompose changeset with multiple concepts. They conducted a user study with students investigating whether their untangling approach affected the time and the correctness in performing review-related tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The sample size comprised in our experiment poses an inherent threat to the internal validity of our experiment. Furthermore, using a different experimental strategy (e.g., that used by Tao & Kim (2015)) would remove personal performance biases, while causing a measurable learning effect. In fact, Wohlin et al (2012) state that "due to learning effects, a human subject cannot apply two methods to the same piece of code."…”

Section: Internal Validitymentioning

confidence: 99%

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

Biase

Bruntink

Deursen

et al. 2019

PeerJ Computer Science

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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 contextseeking), 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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Partitioning Composite Code Changes to Facilitate Code Review

Cited by 45 publications

References 26 publications

Associating working memory capacity and code change ordering with code review performance

Associating working memory capacity and code change ordering with code review performance

On the Optimal Order of Reading Source Code Changes for Review

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

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