Does the Role Matter? An Investigation of the Code Quality of Casual Contributors in GitHub

Lu, Yao; Mao, Xinjun; Li, Zude; Zhang, Yang; Wang, Tao; Yin, Gang

doi:10.1109/apsec.2016.018

Cited by 12 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last feature indicates that most of the external learners perform fewer than three activities in the community. Thus, the majority of external learners are ''casual contributors'' due to their very limited contributions [30]. This finding differs from the OSS context, in which popular OSS projects always attract a group of long-term contributors [31].…”

Section: ) Community Engagementmentioning

confidence: 91%

Studying in the ‘Bazaar’: An Exploratory Study of Crowdsourced Learning in GitHub

Mao

Wang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

In recent years, GitHub, the most popular social coding site, has been increasingly employed for managing learning content, sharing knowledge, imparting experience, and requesting and contributing learning resources in a crowdsourced way. We thus refer to this type of e-learning practices in which people perform learning-related tasks with open calls in an online community to gain knowledge or skills in specific areas as crowdsourced learning (CL). To understand this emerging phenomenon in GitHub, we investigate the popularity of the learning projects and learners' CL activities, first on the extracted GHTorrent learning projects and then on a selected sample of 105 popular learning projects. We then conduct an online survey of 301 learners to qualitatively understand their practices and perceptions of CL in GitHub. Our main findings reveal that the learners' CL practices show some different characteristics from those of open source development, e.g., the learning projects have very few long-term contributors (less than 5% of all contributors). Moreover, although the learners benefit from conducting personalized learning with the high-quality content and contributions made by the community, they encounter challenges in maintaining initiatives to conduct continuous unsupervised learning and in ensuring the quality of content and contributions. Based on the findings, we discuss the reasons behind the growth of CL and provide implications on the CL platform design.

show abstract

Section: ) Community Engagementmentioning

confidence: 91%

Studying in the ‘Bazaar’: An Exploratory Study of Crowdsourced Learning in GitHub

Mao

Wang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since software development is the result of teamwork, factors related to the development team can have a significant impact on software quality. For example, casual developers are likely to introduce quality-related problems of greater severity and in greater quantity [18]. Thus, investigating the factors related to developers and development team may further explain why the selffixing rates vary among different projects.…”

Section: Related Workmentioning

confidence: 99%

Does it matter who pays back Technical Debt? An empirical study of self-fixed TD

Tan¹,

Feitosa²,

Avgeriou³

2021

Preprint

View full text Add to dashboard Cite

Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the corresponding TD issues.Objective: The goal of our study is to investigate self-fixed technical debt, especially the extent in which TD is self-fixed, which types of TD are more likely to be self-fixed, whether the remediation time of self-fixed TD is shorter than non-self-fixed TD and how development behaviors are related to self-fixed TD. Method:We report on an empirical study that analyzes the self-fixed issues of five types of TD (i.e., Code, Defect, Design, Documentation and Test), captured via static analysis, in more than 44,000 commits obtained from 20 Python and 16 Java projects of the Apache Software Foundation. Results:The results show that about half of the fixed issues are self-fixed and that the likelihood of contained TD issues being self-fixed is negatively correlated with project size, the number of developers and total issues. Moreover, there is no significant difference of the survival time between self-fixed and non-selffixed issues. Furthermore, developers are more keen to pay back their own TD when it is related to lower code level issues, e.g., Defect Debt and Code Debt. Finally, developers who are more dedicated to or knowledgeable about

show abstract

“…The code quality plays an essential role for software project's success [5]. Generally, code quality can be classified as internal and external quality attributes [4].…”

Section: Introductionmentioning

confidence: 99%

History-Driven Fix for Code Quality Issues

Xue

Mao

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

To ensure the internal code quality of contributions in open source software (OSS) communities, static analysis tools (e.g. Code Climate and SonarQube) have been integrated into the modern pullbased workflow for detecting code quality issues (CQIs). Automated CQI fixing is conducive to improve the efficiency of converging massive contributions. In this paper, we propose a history-driven approach to automatically fix CQIs utilizing the fixing knowledge mined from the change history in the code repositories. We collected 5,047,678 CQI isstances, 31,013 fixes of the CQIs that were detected by SonarQube from 206 GitHub projects and mined 68 common Fix Patterns for 56 CQI types. Evaluated by fixing the unfix CQI Instances, we find that the average correctness of our approach is 80% in top 1 fix patch and 69.17% of Top 5. We further conducted a live study by sending CQI's fix patches to GitHub projects. The results show that developers approved or merged 11 of 17 patches. INDEX TERMS Open source, code quality, mining GitHub repository, fix pattern.

show abstract

Does the Role Matter? An Investigation of the Code Quality of Casual Contributors in GitHub

Cited by 12 publications

References 22 publications

Studying in the ‘Bazaar’: An Exploratory Study of Crowdsourced Learning in GitHub

Studying in the ‘Bazaar’: An Exploratory Study of Crowdsourced Learning in GitHub

Does it matter who pays back Technical Debt? An empirical study of self-fixed TD

History-Driven Fix for Code Quality Issues

Contact Info

Product

Resources

About