Investigating Code Review Practices in Defective Files: An Empirical Study of the Qt System

Thongtanunam, Patanamon; McIntosh, Shane; Hassan, Ahmed E.; Iida, Hajimu

doi:10.1109/msr.2015.23

Cited by 75 publications

(70 citation statements)

References 34 publications

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“…Investigation of technical factors Different correlations were studied involving technical factors. Thongtanunam et al (2015a)'s study provided evidence that reviewers are less rigorous and find fewer defects on files with a high incidence of defects in the past, focusing on superficial aspects, such as coding standards rather than on functional aspects. In a more recent study (Thongtanunam et al 2016a), the same authors identified that bug fixes typically receive the first feedback faster than implementations of new features.…”

Section: Related Workmentioning

confidence: 99%

“…To understand the factors that positively and negatively affect the effectiveness of code review, previous studies were performed, e.g. (Thongtanunam et al 2015a;Baysal et al 2016;Yang 2014;Bosu et al 2015). Examples of investigated factors are the patch size, the nature of the change, or author's company-that is, both technical and nontechnical factors have been investigated.…”

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confidence: 99%

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Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

Santos

Nunes

2018

J Softw Eng Res Dev

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Code review is a potential means of improving software quality. To be effective, it depends on different factors, and many have been investigated in the literature to identify the scenarios in which it adds quality to the final code. However, factors associated with distributed software development, which is becoming increasingly common, have been little explored. Geographic distance can impose additional challenges to the reviewing process. We thus in this paper present the results of a mixed-method study of the effectiveness of code review in distributed software development. We investigate factors that can potentially influence the outcomes of peer code review. The study involved an analysis of objective data collected from a software project involving 201 members and a survey with 50 practitioners with experience in code review. Our analysis of objective data led to the conclusion that a high number of changed lines of code tends to increase the review duration with a reduced number of messages, while the number of involved teams, locations, and participant reviewers generally improve reviewer contributions, but with a severe penalty to the duration. These results are consistent with those obtained in the survey regarding the influence of factors over duration and participation. However, participants' opinion about the impact on contributions diverges from results obtained from historical data, mainly with respect to distribution.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

Santos

Nunes

2018

J Softw Eng Res Dev

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“…We conduct a case study on two large and successful OSS projects, namely, Qt 3 and OpenStack 4 . These two projects are commonly found in the literature on OSS studies, such as in [2], [3], [4], [6], [9], [18] mainly because these projects contains a large amount of reviewing activity using a code review tool. Figure 5 shows that originally Qt and OpenStack had 70,705 and 92,984 review reports, respectively.…”

Section: Experimental Datasetmentioning

confidence: 99%

The Impact of a Low Level of Agreement Among Reviewers in a Code Review Process

Hirao

Ihara

Ueda

et al. 2016

IFIP Advances in Information and Communication Technology

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Part 1: Full PapersInternational audienceSoftware code review systems are commonly used in software development. In these systems, many patches are submitted to improve the quality. To verify the quality, voting is commonly used by contributors; however, there still exists a major problem, namely, that reviewers do not always simply reach a broad agreement. In our previous study, we found that consensus is not usually reached, implying that an individual reviewer’s final decision usually differs from that of the majority of the other reviewers. In this study, we further investigate the reasons why such situations often occur, and provide suggestions for better handling of these problems. Our analysis of the Qt and OpenStack project datasets allow us to suggest that a patch owner should select more appropriate reviewers who often agree with others’ decisions

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“…Previous research has shown that code review, as a wellestablished software quality practice, is one of the most significant stages in pull-based development [17], [18], [23]. It ensures that only high-quality codes are accepted.…”

Section: Introductionmentioning

confidence: 99%

“…It ensures that only high-quality codes are accepted. Several approaches have been proposed to study on how the code review process influences pull-request acceptance [19], [25] and latency [22], and software release quality [17]. However, a few studies have systematically investigated the automatic identification of what reviewers are talking about in the review discussions which is benificial to better understand the code review process.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Review Comments in Pull-based Development Model

Li¹,

Yu²,

Yin³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-The pull-based model, widely used in distributed software development, allows any contributor to fork a public repository, package contributions as a pull-request, and then merge back to the original repository. Code review is one of the most significant stages in pull-based development. It ensures that only high-quality pull-requests are accepted, based on the in-depth discussion among reviewers. Thus, automatically identifying what reviewers are talking about in the discussions is benificial to better understand the code review process.In this paper, we conduct a case study on three popular opensource software projects hosted on GitHub and construct a finegrained taxonomy including 11 sub-categories for review comments. We then manually label over 5,600 review comments, and propose a Two-Stage Hybrid Classification (TSHC) algorithm to classify review comments automatically by combining rule-based and machine-learning techniques. Comparative experiments with a text-based method achieve a reasonable improvement on each project (9.2% in Rails, 5.3% in Elasticsearch, and 7.2% in Angular.js respectively) in terms of the weighted average Fmeasure.

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Investigating Code Review Practices in Defective Files: An Empirical Study of the Qt System

Cited by 75 publications

References 34 publications

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

The Impact of a Low Level of Agreement Among Reviewers in a Code Review Process

Automatic Classification of Review Comments in Pull-based Development Model

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