Exploring the Advances in Identifying Useful Code Review Comments
- URL: http://arxiv.org/abs/2307.00692v2
- Date: Thu, 6 Jul 2023 16:10:14 GMT
- Title: Exploring the Advances in Identifying Useful Code Review Comments
- Authors: Sharif Ahmed and Nasir U. Eisty
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Effective peer code review in collaborative software development necessitates
useful reviewer comments and supportive automated tools. Code review comments
are a central component of the Modern Code Review process in the industry and
open-source development. Therefore, it is important to ensure these comments
serve their purposes. 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. Finally, it discusses the open problems and challenges in
recognizing useful code review comments for future research.
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) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - Towards debiasing code review support [1.188383832081829]
This paper explores harmful cases caused by cognitive biases during code review.
In particular, we design prototypes covering confirmation bias and decision fatigue.
We show that some techniques could be implemented in existing code review tools.
arXiv Detail & Related papers (2024-07-01T15:58:14Z) - Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective [85.48043537327258]
We propose MANGO (comMents As Natural loGic pivOts), including a comment contrastive training strategy and a corresponding logical comment decoding strategy.
Results indicate that MANGO significantly improves the code pass rate based on the strong baselines.
The robustness of the logical comment decoding strategy is notably higher than the Chain-of-thoughts prompting.
arXiv Detail & Related papers (2024-04-11T08:30:46Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey [6.091233191627442]
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.
arXiv Detail & Related papers (2023-07-26T17:49:19Z) - 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) - 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) - 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.