Code review: Veni, ViDI, vici

Tymchuk, Yuriy; Mocci, Andrea; Lanza, Michele

doi:10.1109/saner.2015.7081825

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…2.1 Background: The code review process Figure 1 depicts a code review (pertaining to the OpenStack project) done with a typical code review tool. Although this is one of the many available review tools, their functionalities are largely the same [65]. In the following we briefly describe each of the components of a review as provided by code review tools.…”

Section: Background and Related Workmentioning

confidence: 99%

Information Needs in Contemporary Code Review

Pascarella

Spadini

Palomba

et al. 2018

Proc. ACM Hum.-Comput. Interact.

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Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers' needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task. Preprint [

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Section: Background and Related Workmentioning

confidence: 99%

Information Needs in Contemporary Code Review

Pascarella

Spadini

Palomba

et al. 2018

Proc. ACM Hum.-Comput. Interact.

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“…Another direction of research for improving code understanding for reviews uses visualization of information. For example, ViDI supports visual design inspection and code quality assessment [245]. The tool uses static code analysis reports to identify critical areas in code, displays the evolution of the amount of issues found in a review session, and allows the reviewer to inspect the impact of code changes.…”

Section: Support Systems For Codementioning

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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“…Optimizing/automating the code review process. By studying tools and techniques supporting code review, Tymchuk et al [47] concluded that popular code review platforms (e.g., Gerrit, Code Flow, Phabricator) mostly offer the same basic functionalities with little support for automating tasks. Such a finding has been confirmed by Pascarella et al [34].…”

Section: Related Workmentioning

confidence: 99%

Using Pre-Trained Models to Boost Code Review Automation

Tufano¹,

Masiero²,

Mastropaolo³

et al. 2022

Preprint

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Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer; the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. While the preliminary results we achieved are encouraging, both models had been tested in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. In this paper, we build on top of that work by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5) model can outperform previous DL models for automating code review tasks. Also, we conducted our experiments on a larger and more realistic (and challenging) dataset of code review activities.

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Code review: Veni, ViDI, vici

Cited by 12 publications

References 25 publications

Information Needs in Contemporary Code Review

Information Needs in Contemporary Code Review

Modern Code Reviews—Survey of Literature and Practice

Using Pre-Trained Models to Boost Code Review Automation

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