Characterization and Prediction of Popular Projects on GitHub

Han, Junxiao; Deng, Shuiguang; Xia, Xin; Wang, Dongjing; Yin, Jianwei

doi:10.1109/compsac.2019.00013

Cited by 25 publications

(16 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate our proposal, we seek developer's opinions thus questionnaires were used. The questionnaire was posted to GitHub developers (GitHub is one of the biggest Open-source software hosting platforms for version control and collaboration, GitHub has millions of developers [7]). The questionnaire has 6 questions, the first three questions were general (asking general questions regarding developer gender, nationality, and programing language they familiar with., We asked about developer preference (audio versus writing).…”

Section: Discussionmentioning

confidence: 99%

Listento Me: An Audio Documentation for Open Source Software

Albayati¹,

Alshomali²,

Holdsworth³

2021

Al-Qadisiyah Journal of Pure Science

View full text Add to dashboard Cite

Abstract— Readme file is a short document associated with most open source software (OSS), it provides useful information about the software. Software developers use different structure when documenting their software, accordingly there were many attempts to uniform the structure of readme file as this file is the main file most user read and help the user to decide upon using the software or not .In this paper we suggest using audio format for readme file (ListenToMe) to do so we used 100 GitHub repositories as a dataset. Extracting commits from it results in 326793 messages. Next, commits regarding documentation have been isolated for further analysis. Finally, we observe the structure of readme file and the number of key attributes used. The result shows that Github developers did not committee to the general Readme file structure suggested by literature[17]. Most developers will not save time to write documentation, thus we suggest an audio version of Readme file. Also structures for the ListentoMe file have been proposed to bridge the gap in documentation structures. To evaluate our suggestion, we conduct aquestionnaire targeting software developers in GitHub for feedbacks. The questionnaire results shows that more than 70% of developers prefer ListentoMe (audio version of ReadMe) , they mostly agreed about the suggested ListentoMe structure.

show abstract

Section: Discussionmentioning

confidence: 99%

Listento Me: An Audio Documentation for Open Source Software

Albayati¹,

Alshomali²,

Holdsworth³

2021

Al-Qadisiyah Journal of Pure Science

View full text Add to dashboard Cite

show abstract

“…We obtained all open source projects in GitHub under all accounts (official account and team accounts) of BAT, to make the analysis more comprehensive. Notably, Then, we removed projects that were forked or empty, removed projects with less than 6 commits (following [2 0 ]) and 2 contributors (following [20], [17]). Ultimately, 1,000 open source projects remained, which are analyzed in this paper.…”

Section: B Project Analysis 1) Data Collectionmentioning

confidence: 99%

An Empirical Study of the Landscape of Open Source Projects in Baidu, Alibaba, and Tencent

Han

Chen

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Self Cite

View full text Add to dashboard Cite

Open source software has drawn more and more attention from researchers, developers and companies nowadays. Meanwhile, many Chinese technology companies are embracing open source and choosing to open source their projects. Nevertheless, most previous studies are concentrated on international companies such as Microsoft or Google, while the practical values of open source projects of Chinese technology companies remain unclear. To address this issue, we conduct a mixed-method study to investigate the landscape of projects open sourced by three large Chinese technology companies, namely Baidu, Alibaba, and Tencent (BAT). We study the categories and characteristics of open source projects, the developer's perceptions towards open sourcing effort for these companies, and the internationalization effort of their open source projects. We collected 1,000 open source projects that were open sourced by BAT in GitHub and performed an online survey that received 101 responses from developers of these projects. Some key findings include: 1) BAT prefer to open source frontend development projects, 2) 88% of the respondents are positive towards open sourcing software projects in their respective companies, 3) 64% of the respondents reveal that the most common motivations for BAT to open source their projects are the desire to gain fame, expand their influence and gain recruitment advantage , 4) respondents believe that the most common internationalization effort is "providing an English version of readme files", 5) projects with more internationalization effort (i.e., include an English readme file) are more popular. Our findings provide directions for software engineering researchers and provide practical suggestions to software developers and Chinese technology companies.

show abstract

“…(3) Repository selection rationale: We select the top 100 repositories provided by the GitHub RESTful API for each input category. The additional constraints [Coelho et al 2020;Han et al 2019] applied to filter the OSS repositories under each of the selected software categories (selected in Step 1 above) are:…”

Section: R Kapur and B Sodhimentioning

confidence: 99%

OSS effort estimation using software features similarity and developer activity-based metrics

Kapur,

Sodhi

2021

Preprint

View full text Add to dashboard Cite

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy.We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our paper are i) Novel SDEE software metrics derived from developer activity information of various software repositories, ii) SDEE dataset comprising the SDEE metrics' values derived from ≈ 13, 000 GitHub repositories from 150 different software categories, iii) an effort estimation tool based on SDEE metrics and a software description similarity model. Our software description similarity model is basically a machine learning model trained using the Paragraph Vectors algorithm on the software product descriptions of GitHub repositories. Given the software description of a newly-envisioned software, our tool yields an effort estimate for developing it.Our method achieves the highest Standard Accuracy score of 87.26% (with cliff's 𝛿=0.88 at 99.999% confidence level) and 42.7% with the Automatic Transformed Linear Baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723. CCS Concepts: • Software and its engineering → Design patterns; Software development process management.

show abstract

Characterization and Prediction of Popular Projects on GitHub

Cited by 25 publications

References 17 publications

Listento Me: An Audio Documentation for Open Source Software

Listento Me: An Audio Documentation for Open Source Software

An Empirical Study of the Landscape of Open Source Projects in Baidu, Alibaba, and Tencent

OSS effort estimation using software features similarity and developer activity-based metrics

Contact Info

Product

Resources

About