Deciphering Refactoring Branch Dynamics in Modern Code Review: An Empirical Study on Qt
- URL: http://arxiv.org/abs/2410.04678v1
- Date: Mon, 7 Oct 2024 01:18:56 GMT
- Title: Deciphering Refactoring Branch Dynamics in Modern Code Review: An Empirical Study on Qt
- Authors: Eman Abdullah AlOmar,
- Abstract summary: This study aims to understand the review process for changes in the Refactor branch and to identify what developers care about when reviewing code in this branch.
We find that reviews involving from the Refactor branch take significantly less time to resolve in terms of code review.
Additionally, documentation of developer intent is notably sparse within the Refactor branch compared to other branches.
- Score: 5.516979718589074
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Context: Modern code review is a widely employed technique in both industrial and open-source projects, serving to enhance software quality, share knowledge, and ensure compliance with coding standards and guidelines. While code review is extensively studied for its general challenges, best practices, outcomes, and socio-technical aspects, little attention has been paid to how refactoring is reviewed and what developers prioritize when reviewing refactored code in the Refactor branch. Objective: The goal is to understand the review process for refactoring changes in the Refactor branch and to identify what developers care about when reviewing code in this branch. Method: In this study, we present a quantitative and qualitative examination to understand the main criteria developers use to decide whether to accept or reject refactored code submissions and identify the challenges inherent in this process. Results: Analyzing 2,154 refactoring and non-refactoring reviews across Qt open-source projects, we find that reviews involving refactoring from the Refactor branch take significantly less time to resolve in terms of code review efforts. Additionally, documentation of developer intent is notably sparse within the Refactor branch compared to other branches. Furthermore, through thematic analysis of a substantial sample of refactoring code review discussions, we construct a comprehensive taxonomy consisting of 12 refactoring review criteria.
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) - DOCE: Finding the Sweet Spot for Execution-Based Code Generation [69.5305729627198]
We propose a comprehensive framework that includes candidate generation, $n$-best reranking, minimum Bayes risk (MBR) decoding, and self-ging as the core components.
Our findings highlight the importance of execution-based methods and the difference gap between execution-based and execution-free methods.
arXiv Detail & Related papers (2024-08-25T07:10:36Z) - MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability [6.439206681270567]
It is essential to ensure that the searched sequence of sequences can be reviewed promptly.
We propose MORCoRA, a multi-objective search-based technique that can search for code quality, semantic preserved, and high review availability.
arXiv Detail & Related papers (2024-08-13T02:08:16Z) - In Search of Metrics to Guide Developer-Based Refactoring Recommendations [13.063733696956678]
Motivation is a well-established approach to improving source code quality without compromising its external behavior.
We propose an empirical study into the metrics that study the developer's willingness to apply operations.
We will quantify the value of product and process metrics in grasping developers' motivations to perform.
arXiv Detail & Related papers (2024-07-25T16:32:35Z) - Insights into Deep Learning Refactoring: Bridging the Gap Between Practices and Expectations [13.084553746852382]
Deep learning software has become progressively complex as the software evolves.
The insight of code in the context of deep learning is still unclear.
Research and the development of related tools are crucial for improving project maintainability and code quality.
arXiv Detail & Related papers (2024-05-08T07:35:14Z) - State of Refactoring Adoption: Better Understanding Developer Perception
of Refactoring [5.516979718589074]
We aim to explore how developers document their activities during the software life cycle.
We call such activity Self-Affirmed Refactoring (SAR), which indicates developers' documentation of their activities.
We propose an approach to identify whether a commit describes developer-related events to classify them according to the common quality improvement categories.
arXiv Detail & Related papers (2023-06-09T16:38:20Z) - Do code refactorings influence the merge effort? [80.1936417993664]
Multiple contributors frequently change the source code in parallel to implement new features, fix bugs, existing code, and make other changes.
These simultaneous changes need to be merged into the same version of the source code.
Studies show that 10 to 20 percent of all merge attempts result in conflicts, which require the manual developer's intervention to complete the process.
arXiv Detail & Related papers (2023-05-10T13:24:59Z) - How We Refactor and How We Document it? On the Use of Supervised Machine
Learning Algorithms to Classify Refactoring Documentation [25.626914797750487]
Refactoring is the art of improving the design of a system without altering its external behavior.
This study categorizes commits into 3 categories, namely, Internal QA, External QA, and Code Smell Resolution, along with the traditional BugFix and Functional categories.
To better understand our classification results, we analyzed commit messages to extract patterns that developers regularly use to describe their smells.
arXiv Detail & Related papers (2020-10-26T20:33:17Z) - 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.