{"title":"Taxonomy of inline code comment smells","authors":"","doi":"10.1007/s10664-023-10425-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments to explain a code snippet, and commenting code is generally considered a good practice in software engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how frequently each smell type occurs in software projects. We conducted a multivocal literature review to define the initial taxonomy of inline comment smells. Afterward, we manually labeled 2447 inline comments from eight open-source projects where half of them were Java, and another half were Python projects. We created a taxonomy of 11 inline code comment smell types and found out that the smells exist in both Java and Python projects with varying degrees. Moreover, we conducted an online survey with 41 software practitioners to learn their opinions on these smells and their impact on code comprehension and software maintainability. The survey respondents generally agreed with the taxonomy; however, they reported that some smell types might have a positive effect on code comprehension in certain scenarios. We also opened pull requests and issues fixing the comment smells in the sampled projects, where we got a 27% acceptance rate. We share our manually labeled dataset online and provide implications for software engineering practitioners, researchers, and educators.</p>","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"114 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Empirical Software Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10664-023-10425-5","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments to explain a code snippet, and commenting code is generally considered a good practice in software engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how frequently each smell type occurs in software projects. We conducted a multivocal literature review to define the initial taxonomy of inline comment smells. Afterward, we manually labeled 2447 inline comments from eight open-source projects where half of them were Java, and another half were Python projects. We created a taxonomy of 11 inline code comment smell types and found out that the smells exist in both Java and Python projects with varying degrees. Moreover, we conducted an online survey with 41 software practitioners to learn their opinions on these smells and their impact on code comprehension and software maintainability. The survey respondents generally agreed with the taxonomy; however, they reported that some smell types might have a positive effect on code comprehension in certain scenarios. We also opened pull requests and issues fixing the comment smells in the sampled projects, where we got a 27% acceptance rate. We share our manually labeled dataset online and provide implications for software engineering practitioners, researchers, and educators.
期刊介绍：
Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories.
The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings.
Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
