Modern code reviews - Preliminary results of a systematic mapping study

Badampudi, Deepika; Britto, Ricardo; Unterkalmsteiner, Michael

doi:10.1145/3319008.3319354

Cited by 17 publications

(25 citation statements)

References 109 publications

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“…Code review request prioritisation is not a topic widely addressed by the software engineering community [25]. In addition to that, there was no empirical evidence of the usefulness and feasibility of this type of approach in real live industrial environments, since all existing studies have only evaluated their respective proposals from a predictive performance point of view (through historical data).…”

Section: Discussionmentioning

confidence: 99%

Using Machine Intelligence to Prioritise Code Review Requests

Saini

Britto

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Self Cite

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Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually prioritising review requests is a challenging and timeconsuming process. To address the above problem, we conducted an industrial case study at Ericsson aiming at developing a tool called Pineapple, which uses a Bayesian Network to prioritise code review requests. To validate our approach/tool, we deployed it in a live software development project at Ericsson, wherein more than 150 developers develop a telecommunication product. We focused on evaluating the predictive performance, feasibility, and usefulness of our approach. The results indicate that Pineapple has competent predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6% of Pineapple's users believe the tool can support code review request prioritisation by providing reliable results, and around 56.5% of the users believe it helps reducing code review lead time. As future work, we plan to evaluate Pineapple's predictive performance, usefulness, and feasibility through a longitudinal investigation.

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Section: Discussionmentioning

confidence: 99%

Using Machine Intelligence to Prioritise Code Review Requests

Saini

Britto

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Self Cite

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“…To contrast ethics issues in data collection with ethics issues in data analysis, the second case study is about a project in which Gerrit repositories together with their linked Git repositories were analysed in order to recommend a suitable code reviewer for a change based on data about reviewers and authors from past changes. This is a problem that has been the focus of many previous papers (Badampudi et al 2019).…”

Section: Case 2: Reviewer Recommendationmentioning

confidence: 99%

Ethics in the mining of software repositories

Gold

Krinke

2021

Empir Software Eng

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Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers’ and users’ interactions with the repositories and with each other. The ethics issues raised by such research therefore need to be considered before beginning. This paper presents a discussion of ethics issues that can arise in MSR research, using the mining challenges from the years 2006 to 2021 as a case study to identify the kinds of data used. On the basis of contemporary research ethics frameworks we discuss ethics challenges that may be encountered in creating and using repositories and associated datasets. We also report some results from a small community survey of approaches to ethics in MSR research. In addition, we present four case studies illustrating typical ethics issues one encounters in projects and how ethics considerations can shape projects before they commence. Based on our experience, we present some guidelines and practices that can help in considering potential ethics issues and reducing risks.

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“…To mitigate the limitations of our SLR in the review planning phase, we selected widely used digital libraries as sources and specified keywords as search string that cover the studied theme, assuming that the selected strategy would retrieve the largest number of relevant studies. Three of our selected databases-ACM Digital Library, IEEE Xplore, SpringerLink-were also used in other secondary studies on MCR [8,7,10]. While these other studies also searched the Scopus database, we selected Sci-enceDirect as a source.…”

Section: Research Limitationsmentioning

confidence: 99%

“…Three systematic mapping studies were done on this topic. Two [7,8] aimed at identifying themes that are typically target of research, while that made by Coelho et al [9] focused on refactoring-aware code review. In addition, there are systematic literature reviews on MCR with a narrower scope than ours.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review and Taxonomy of Modern Code Review

Davila,

Nunes

2021

Preprint

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Context: Modern Code Review (MCR) is a widely known practice of software quality assurance. However, the existing body of knowledge of MCR is currently not understood as a whole. Objective: Our goal is to identify the state of the art on MCR, providing a structured overview and an in-depth analysis of the research done in this field. Method: We performed a systematic literature review, selecting publications from four digital libraries. Results: A total of 139 papers were selected and analyzed in three main categories. Foundational studies are those that analyze existing or collected data from the adoption of MCR. Proposals consist of techniques and tools to support MCR, while evaluations are studies to assess an approach or compare a set of them. Conclusion: The most represented category is foundational studies, mainly aiming to understand the motivations for adopting MCR, its challenges and benefits, and which influence factors lead to which MCR outcomes. The most common types of proposals are code reviewer recommender and support to code checking. Evaluations of MCR-supporting approaches have been done mostly offline, without involving human subjects. Five main research gaps have been identified, which point out directions for future work in the area.

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Modern code reviews - Preliminary results of a systematic mapping study

Cited by 17 publications

References 109 publications

Using Machine Intelligence to Prioritise Code Review Requests

Using Machine Intelligence to Prioritise Code Review Requests

Ethics in the mining of software repositories

A Systematic Literature Review and Taxonomy of Modern Code Review

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