.png){kind=icon}
返回
Understanding Code Understandability Improvements in Code Reviews
.png){kind=icon}
OA
AI
DOI：10.1109/TSE.2024.3453783.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Context: Code understandability plays a crucial role in software development, as developers spend between 58% and 70% of their time reading source code. Improving code understandability can lead to enhanced productivity and save maintenance costs. Problem: Experimental studies aim to establish what makes code more or less understandable in a controlled setting, but ignore that what makes code easier to understand in the real world also depends on extraneous elements such as developers' background and project culture and guidelines. Not accounting for the influence of these factors may lead to results that are sound but have little external validity. Goal: We aim to investigate how developers improve code understandability during software development through code review comments. Our assumption is that code reviewers are specialists in code quality within a project. Method and Results: We manually analyzed 2,401 code review comments from Java open-source projects on GitHub and found that over 42% of all comments focus on improving code understandability, demonstrating the significance of this quality attribute in code reviews. We further explored a subset of 385 comments related to code understandability and identified eight categories of code understandability concerns, such as incomplete or inadequate code documentation, bad identifier, and unnecessary code. Among the suggestions to improve code understandability, 83.9% were accepted and integrated into the codebase. Among these, only two (less than 1%) ended up being reverted later. We also identified types of patches that improve code understandability, ranging from simple changes (e.g., removing unused code) to more context-dependent improvements (e.g., replacing method calling chains by existing API). Finally, we investigated the potential coverage of four well-known linters to flag the identified code understandability issues. These linters cover less than 30% of these issues, although some of them could be easily added as new rules. Implications: Our findings motivate and provide practical insight for the construction of tools to make code more understandable, e.g., understandability improvements are rarely reverted and thus can be used as reliable training data for specialized ML-based tools. This is also supported by our dataset, which can be used to train such models. Finally, our findings can also serve as a basis to develop evidence-based code style guides.
Keyword:
Codes
Reviews
Source coding
Software development management
Documentation
Security
Natural languages
Code understandability
code understandability smells
code review
期刊
IF:
5.6
论文数:
2.9K
被引数:
1.1W
机构
引用论文
?-1,4-Glucosidase activity in infertile oligoasthenozoospermic men with and without varicocele.png)
Andrologia
OAAI
AndrologiaIF0
OAAI分享收藏The Adoption of JavaScript Linters in Practice: A Case Study on ESLintJavaScript linter在实践中的应用: ESLint的案例研究OAAI
OAAI分享收藏A graph-based code representation method to improve code readability classification一种基于图的代码表示方法提高代码可读性分类
PREAI
分享收藏
查看更多内容