Understanding the Factors That Impact the Popularity of GitHub Repositories

Borges, Hudson Silva; Hora, André; Valente, M.

doi:10.1109/icsme.2016.31

Cited by 235 publications

(198 citation statements)

References 28 publications

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Section: Study Resultssupporting

confidence: 94%

“…Project forks ( nForks ) also has a significant positive effect (24.2% of the variance explained in Basic model ; 19.6% of the variance explained in Advanced model ), ie, more forks, more popular . Those findings are in line with the general expectations and are consistent with the results found in the study of Borges et al For all projects in our dataset, the factor useMilestoneTag has a significant positive effect (1.8% of the variance explained) on the number of project stars. A one‐unit increase in useMilestoneTag results in the increase in project stars of 12.3%, holding all other variables constant.…”

Section: Study Resultssupporting

confidence: 93%

“…Using the milestone tool for the development task management may help developers focus more on coding, making projects release more frequently, further making the project more successful and more popular. Thus, the outcome (dependent) variables of the models are: • nCommits : number of project commits, as a proxy for the project commit productivity; • nRelease : number of project releases, as a proxy for the project release quantity; • nStars : number of project stars, as a proxy for the project popularity …”

Section: Methodsmentioning

confidence: 99%

“…• nStars: number of project stars, as a proxy for the project popularity. 27 Our independent variables of the models are mainly based on prior studies 2,4,28,29 and come from two confound areas: project-level and milestone-level.…”

Section: Regression Variablesmentioning

confidence: 99%

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GitHub's milestone tool: A mixed‐methods analysis on its use

Zhang

Wang

et al. 2019

J Software Evolu Process

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Social coding site GitHub provides developers with many management tools to facilitate project maintenance and developer collaboration. Milestone tool, in particular, plays an important role in organizing and tracking progress on groups of issues or pull requests in a project. However, few research has analyzed the milestone tool, even though it has been used in practice for a long time. In this paper, we want to address this literature gap and present an ongoing work aimed at investigating the use of the milestone tool in GitHub open‐source projects. We conduct a mixed‐methods analysis in a large‐scale dataset of GitHub projects, to help developers gain some insights into the milestone tool, including its usage, benefits, and limitations. We quantitatively investigate the basic adoption of milestone tool and its correlation with project properties. We also survey developers to understand the reasons for using milestone tool or not and their perceptions of the milestone tool. We find that certain types of projects use milestone tool more than others. Adopting the milestone tool is associated with more commits, more releases, and more project popularity, but the current milestone tool also has some limitations. These observations can then be forwarded to the GitHub community for follow‐up and can result in them potentially making a better milestone tool.

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Section: Study Resultssupporting

confidence: 94%

Section: Study Resultssupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Regression Variablesmentioning

confidence: 99%

See 2 more Smart Citations

GitHub's milestone tool: A mixed‐methods analysis on its use

Zhang

Wang

et al. 2019

J Software Evolu Process

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“…Stars indicate the popularity of a project and are given by GitHub users [10]. We assumed that the most popular projects relect those applications that are most likely processed by C tools.…”

Section: Obtaining the Projectsmentioning

confidence: 99%

An Analysis of x86-64 Inline Assembly in C Programs

Rigger

Marr

Kell

et al. 2018

Proceedings of the 14th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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C codebases frequently embed nonportable and unstandardized elements such as inline assembly code. Such elements are not well understood, which poses a problem to tool developers who aspire to support C code. This paper investigates the use of x86-64 inline assembly in 1264 C projects from GitHub and combines qualitative and quantitative analyses to answer questions that tool authors may have. We found that 28.1% of the most popular projects contain inline assembly code, although the majority contain only a few fragments with just one or two instructions. The most popular instructions constitute a small subset concerned largely with multicore semantics, performance optimization, and hardware control. Our indings are intended to help developers of C-focused tools, those testing compilers, and language designers seeking to reduce the reliance on inline assembly. They may also aid the design of tools focused on inline assembly itself.

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Investigating diversity and impact of the popularity metrics for ranking software packages

Saini

Verma

Singh

et al. 2020

J Software Evolu Process

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Context Community‐based collaborative approach in open source software paradigm promotes reuse of existing software packages. There are several repositories (e.g., npm) for packages and have their own set of metrics for ranking. Objective This study explores the diversity of different popularity metrics and also the relationship between popularity metrics and development activity of the packages. Another aim is to create a package popularity index by aggregating a set of noncollinear popularity metrics. Method Using 195 K packages from different repositories, we investigated the correlation between different popularity metrics. K‐medoids algorithm helped to identify packages with different levels of popularity. Random forests method is utilized to create the package popularity index. Lastly, we used scikit‐learn implementation for determining feature importance in the model. Results Popularity metrics of the Github platform are very strongly correlated (R ≥ 0.85) for highly popular packages. Popular packages have high‐development activity. However, the number of downloads of a package does not associate with development activity. Not all the metrics are important for determining popularity of a software package. Conclusion This study provides practical guidelines to understand important metrics to determine the popularity of software packages. Researchers should focus on non‐collinear metrics, thereby avoiding similar metrics while aggregating for building models.

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Understanding the Factors That Impact the Popularity of GitHub Repositories

Cited by 235 publications

References 28 publications

GitHub's milestone tool: A mixed‐methods analysis on its use

GitHub's milestone tool: A mixed‐methods analysis on its use

An Analysis of x86-64 Inline Assembly in C Programs

Investigating diversity and impact of the popularity metrics for ranking software packages

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