Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey

• URL: http://arxiv.org/abs/2307.14406v3
• Date: Thu, 4 Apr 2024 06:55:55 GMT
• Title: Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey
• Authors: Beiqi Zhang, Liming Fu, Peng Liang, Jiaxin Yu, Chong Wang,
• Abstract summary: We conduct a mixed-methods study to mine information and knowledge related to code snippets in code reviews. The study results highlight that reviewers can provide code snippets in appropriate scenarios to meet developers' specific information needs in code reviews.
• Score: 6.091233191627442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code review is widely known as one of the best practices for software quality assurance in software development. In a typical code review process, reviewers check the code committed by developers to ensure the quality of the code, during which reviewers and developers would communicate with each other in review comments to exchange necessary information. As a result, understanding the information in review comments is a prerequisite for reviewers and developers to conduct an effective code review. Code snippet, as a special form of code, can be used to convey necessary information in code reviews. For example, reviewers can use code snippets to make suggestions or elaborate their ideas to meet developers' information needs in code reviews. However, little research has focused on the practices of providing code snippets in code reviews. To bridge this gap, we conduct a mixed-methods study to mine information and knowledge related to code snippets in code reviews, which can help practitioners and researchers get a better understanding about using code snippets in code review. Specifically, our study includes two phases: mining code review data and conducting practitioners' survey. The study results highlight that reviewers can provide code snippets in appropriate scenarios to meet developers' specific information needs in code reviews, which will facilitate and accelerate the code review process.

Related papers

• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• Help Me to Understand this Commit! -- A Vision for Contextualized Code Reviews [4.87707664110891]
  We aim to provide a vision of improving code understanding in Modern Code Review. We identify four major types of support systems and suggest an environment for contextualized code reviews.
  arXiv  Detail & Related papers  (2024-02-14T19:08:47Z)
• Exploring the Advances in Identifying Useful Code Review Comments [0.0]
  This paper reflects the evolution of research on the usefulness of code review comments. It examines papers that define the usefulness of code review comments, mine and annotate datasets, study developers' perceptions, analyze factors from different aspects, and use machine learning classifiers to automatically predict the usefulness of code review comments.
  arXiv  Detail & Related papers  (2023-07-03T00:41:20Z)
• Code Execution with Pre-trained Language Models [88.04688617516827]
  Most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension.
  arXiv  Detail & Related papers  (2023-05-08T10:00:05Z)
• What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigation [4.061135251278187]
  Even a minor improvement in the effectiveness of Code Reviews can incur significant savings for a software development organization. This study aims to develop a finer grain understanding of what makes a code review comment useful to OSS developers.
  arXiv  Detail & Related papers  (2023-02-22T22:48:27Z)
• CodeReviewer: Pre-Training for Automating Code Review Activities [36.40557768557425]
  This research focuses on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review senario.
  arXiv  Detail & Related papers  (2022-03-17T05:40:13Z)
• CodeRetriever: Unimodal and Bimodal Contrastive Learning [128.06072658302165]
  We propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations. For unimodal contrastive learning, we design a semantic-guided method to build positive code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.
  arXiv  Detail & Related papers  (2022-01-26T10:54:30Z)
• Predicting Code Review Completion Time in Modern Code Review [12.696276129130332]
  Modern Code Review (MCR) is being adopted in both open source and commercial projects as a common practice. Code reviews can experience significant delays to be completed due to various socio-technical factors. There is a lack of tool support to help developers estimating the time required to complete a code review.
  arXiv  Detail & Related papers  (2021-09-30T14:00:56Z)
• COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
  We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic. COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:53:38Z)
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code [51.00904399653609]
  In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code. We develop a deep-learning approach that learns to correlate a comment with code changes. We show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system.
  arXiv  Detail & Related papers  (2020-10-04T16:49:28Z)
• Code Review in the Classroom [57.300604527924015]
  Young developers in a classroom setting provide a clear picture of the potential favourable and problematic areas of the code review process. Their feedback suggests that the process has been well received with some points to better the process. This paper can be used as guidelines to perform code reviews in the classroom.
  arXiv  Detail & Related papers  (2020-04-19T06:07:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.