Using Machine Learning to Identify Code Fragments for Manual Review

Staron, Miroslaw; Ochodek, Mirosław; Meding, Wilhelm; Söder, Ola

doi:10.1109/seaa51224.2020.00085

Cited by 6 publications

(2 citation statements)

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“…We were not able to locate any such studies in the 13 journals that we had previously searched systematically. However, we did find seven articles in a variety of other journals and conferences (Fricke, 2018;Kennedy et al, 2015;Lim et al, 2018;Lwakatare et al, 2021;Nzembayie et al, 2019;Staron et al, 2020;Sundarakani et al, 2021). None of these seven articles made any mention of ethical issues and in reading them we could not identify any ethical issues either.…”

Section: A New Principle Of Ethical Participationmentioning

confidence: 88%

The ethics of action research participation

Davison

Martinsons

Wong

2021

Information Systems Journal

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Action research (AR) involves one or more researchers and a client organisation. Many guidelines for and reports of the research method have been published. However, the ethical issues associated with AR have been largely neglected. Our review of the AR literature found that ethical dilemmas and their resolution are rarely and inconsistently reported. Stimulated by this neglect and our personal experiences, we aim to raise awareness and understanding about the ethics of planning, conducting and reporting AR. We identify and discuss four issues of concern that merit specific ethical attention when conducting AR: collaboration, competence, persistence and consent. We draw on these four issues in an analysis that augments the principles and criteria for canonical AR (CAR), recently reified as Integrated Action Research (IAR). Our guidance includes an additional principle of AR and 10 associated criteria to address the ethics of AR participation.

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Section: A New Principle Of Ethical Participationmentioning

confidence: 88%

The ethics of action research participation

Davison

Martinsons

Wong

2021

Information Systems Journal

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“…Another idea to reduce review effort is to prioritize code that is likely to exhibit issues. One approach is to train a Convolutional Neural Network with old review comments and source code features to identify code fragments that require review [229]. Similarly, CRUSO classifies to be reviewed code by identifying similar code snippets on StackOverflow and analyzing the corresponding comments and meta-data, leveraging crowd-knowledge [148,217].…”

Section: Mcr Themes and Contributionsmentioning

confidence: 99%

Modern Code Reviews—Survey of Literature and Practice

Badampudi

Unterkalmsteiner

Britto

2023

ACM Trans. Softw. Eng. Methodol.

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Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist; it is unknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state-of-the-art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor- and support systems-related research. Conclusion: These results indicate a misalignment between the state-of-the-art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda, which can potentially increase the impact of MCR research.

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