GitHub Users Research Articles

Productivity losses in the transition to Daylight Saving Time: Evidence from hourly GitHub activity

Using data on GitHub users around the world, we estimate the effects of transitions to Daylight Saving Time on worker activity. In daily activity, transitions appear short lived—there is evidence of two days of declines before activity returns to baseline levels. However, hourly analysis reveals a transition to Daylight Saving Time that is much longer—losses appear in the early working hours of work days into a second week following the initiation of Daylight Saving Time.

Who makes open source code? The hybridisation of commercial and open source practices

While Free and Open Source (F/OSS) coding has traditionally been described as a separate commons linked to values of openness and sharing, recent research suggests an increasing integration of private corporations into F/OSS practices, blurring the boundaries between F/OSS and commodified coding. However, there is a dearth of empirical, and especially quantitative studies exploring this phenomenon. To address this gap, we model the power dynamics and infrastructural aspects of software production within GitHub, a central hub for F/OSS development, using a large-scale, directed network. Using various network statistics, we detect the ecosystem’s most impactful actors and find a nuanced picture of the influence of individuals, open source organizations, and private corporations in F/OSS practices. We find that the majority of public repositories on GitHub depend on a small core of specialized repositories and users. In accordance with expectations, individuals and open source organizations are more prevalent in this core of elite GitHub users, however, we also find a significant amount of private organizations with an indirect, yet consistent influence within GitHub. In addition, we find that directly influential individuals tend to facilitate sponsorship methods more often than indirectly or non-influential individuals. Our research highlights a hybridization of F/OSS and sheds light on the complex interplay between influence, power, and code production in the multi-language dependency ecosystem of GitHub.

A User Evaluation of a Collaborator Recommender based on Co-Changed Files

Active collaboration is essential for the success of software projects across the development life-cycle. Unfortunately, in social coding platforms, such as GitHub, it is still challenging for developers to identify potential collaborators with whom they could engage to create new/stronger ties and enhance the quality of contributions. To this end, we implemented developer recommendation strategies and prototype tool to help project contributors improve their collaborations. Thus, in this work, we described a controlled experimental study concerned usability and user satisfaction to investigate the developers’ perceptions of using CoopFinder, a prototype tool to support two strategies for recommending collaborations. These developer recommendation strategies aim to connect developers of a specific project based on their similar interests. The study involved 35 participants, 18 of which were GitHub users, and 17 were non–GitHub users. We asked participants to perform the experiment tasks to find collaborators with similar interests using a prototype recommendation tool and GitHub. We reported a quantitative and qualitative evaluation of strategies and tool using the state of the practice as a baseline. As a result, we observed that recommender based on co–changed files can provide suitable collaborator recommendations to developers of a specific project. About 66% of the participants confirmed they would use or recommend this tool.

Detecting Malicious Accounts in Online Developer Communities Using Deep Learning

Online developer communities like GitHub allow a massive number of developers to collaborate. However, the openness of the communities makes them vulnerable to different types of malicious attacks, since attackers can easily join these communities and interact with legitimate users. In this work, we propose GitSec, a deep learning-based solution for detecting malicious accounts in online developer communities. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles, dynamic activity characteristics, as well as social interactions. First, GitSec introduces two user activity sequences and applies a parallel neural network design with an attention mechanism to process the sequences. Second, GitSec constructs two graphs to represent the interactions between users according to their repository operations. Especially, graph neural networks and structural hole theory are employed to deal with the two constructed graphs. Third, GitSec makes use of the descriptive features to enhance the detection performance. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our comprehensive evaluations show that GitSec achieves a better performance than state-of-the-art solutions, with an AUC value of 0.916.

Assign-MALDI - A free software for assignment of MALDI-TOF MS spectra of glycans derivatized using common and novel labeling strategies.

Matrix Assisted Laser Desorption/Ionization Time-of-flight (MALDI-ToF) MS is a popular method to analyze glycans released from proteins, cell lines, and tissue samples. Chemical modification of glycans (derivatization) can enhance ionization, enable semi-quantitation, and assist in linkage identification. However, the mass changes incurred by novel and more recently developed derivatizations are not accommodated by most spectral assignment programs, necessitating manual assignment which increases both the difficultly and the likelihood of error. AssignMALDI is a software tool designed to create glycan databases with customized derivatizations (labels) and automatically assign glycan masses in MALDI-TOF spectra using the new database. It can also average peak intensities across multiple spectra and prepare publication-ready assignment tables. To make it easy to use with different platforms, all input files and most output files are in text format. An interactive display enables users to inspect and edit peak assignments prior to producing charts and tables for publication. The program is freely available through GitHUB and Python-savvy users can add or adjust features as needed.

Find potential partners: A GitHub user recommendation method based on event data

GitHub has attracted much popularity among a large number of software developers around the world and introduced the social function follow to strengthen the relationship among developers. Like other social networks, GitHub users usually follow others who are popular in the community, co-workers, or friends in real life. However, according to our investigation, more than half of GitHub users prefer to follow recently like-minded developers other than their traditional networks for communicating with timely feedback, discovering niche repositories, and attracting more active contributors to cooperate, while these users are hard to find.

GgVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram

Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).

A Comparative Analysis of the Use of GitHub by Librarians and Non-Librarians

Abstract
 Objective – GitHub is a popular tool that allows software developers to collaborate and share their code on the web. Librarians have adopted GitHub to support their own work, sharing code in support of their libraries. This paper asks: How does librarians’ use of GitHub compare to that of other users?
 Methods – To retrieve quantitative data on GitHub users, we queried the GitHub APIs (application programming interfaces). By assembling data on librarians’ use of GitHub, as well as on a comparison group, we provided preliminary comparisons of these two samples. We analyzed and visualized this data across a number of variables to offer salient insights as to how librarians compare to randomly selected GitHub users.
 Results – Librarians regularly use a more diverse range of programming languages than the comparison group, hinting at a broad range of possible uses of code in libraries. While the librarians’ sample group did not demonstrate statistically significant differences from the comparison group on most measures of activity and popularity, they scored significantly higher in reach and productivity than the comparison group. This could be due to librarians’ greater longevity on GitHub, as well as their greater investment in GitHub as a tool for sharing.
 Conclusion – Our data suggest that librarians are actively building their libraries with code and sharing the results. While it was unclear whether librarians were more active or popular on GitHub than the comparison group, it was clear that they demonstrated statistically significant outperformance in terms of reach and productivity. To explain these findings, we hypothesized that librarians’ embrace of GitHub is in line with widely held values of “openness” in the library profession.

Mining the Technical Roles of GitHub Users

Abstract Context:Modern software development demands high levels of technical specialization. These conditions make IT companies focus on creating cross-functional teams, such as frontend, backend, and mobile developers. In this context, the success of software projects is highly influenced by the expertise of these teams in each field. Objective:In this paper, we investigate machine-learning based approaches to automatically identify the technical roles of open source developers. Method:For this, we first build a ground truth with 2284 developers labeled in six different roles: backend, frontend, full-stack, mobile, devops, and data science. Then, we build three different machine-learning models used to identify these roles. Results:These models presented competitive results for precision (0.88) and AUC (0.89) when identifying all six roles. Moreover, our results show that programming-languages are the most relevant features to predict the investigated roles. Conclusion:The approach proposed in this paper can assist companies during their hiring process, such as by recommending developers with the expertise required by job positions.

How are issue reports discussed in Gitter chat rooms?

Informal communication channels like mailing lists, IRC and instant messaging play a vital role in open source software development by facilitating communication within geographically diverse project teams e.g., to discuss issue reports to facilitate the bug-fixing process. More recently, chat systems like Slack and Gitter have gained a lot of popularity and developers are rapidly adopting them. Gitter is a chat system that is specifically designed to address the needs of GitHub users. Gitter hosts project-based asynchronous chats which foster frequent project discussions among participants. Developer discussions contain a wealth of information such as the rationale behind decisions made during the evolution of a project. In this study, we explore 24 open source project chat rooms that are hosted on Gitter, containing a total of 3,133,106 messages and 14,096 issue references. We manually analyze the contents of chat room discussions around 457 issue reports. The results of our study show the prevalence of issue discussions on Gitter, and that the discussed issue reports have a longer resolution time than the issue reports that are never brought on Gitter.

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Besides a git-based version control system, GitHub integrates several social coding features. Particularly, GitHub users can star a repository, presumably to manifest interest or satisfaction with an open source project. However, the real and practical meaning of starring a project was never the subject of an in-depth and well-founded empirical investigation. Therefore, we provide in this paper a throughout study on the meaning, characteristics, and dynamic growth of GitHub stars. First, by surveying 791 developers, we report that three out of four developers consider the number of stars before using or contributing to a GitHub project. Then, we report a quantitative analysis on the characteristics of the top-5,000 most starred GitHub repositories. We propose four patterns to describe stars growth, which are derived after clustering the time series representing the number of stars of the studied repositories; we also reveal the perception of 115 developers about these growth patterns. To conclude, we provide a list of recommendations to open source project managers (e.g., on the importance of social media promotion) and to GitHub users and Software Engineering researchers (e.g., on the risks faced when selecting projects by GitHub stars).

User influence analysis for Github developer social networks

Github, one of the largest social coding platforms, offers software developers the opportunity to engage in social activities relating to software development and to store or share their codes/projects with the wider community using the repositories. Analysis of data representing the social interactions of Github users can reveal a number of interesting features. In this paper, we analyze the data to understand user social influence on the platform. Specifically, we propose a Following-Star-Fork-Activity based approach to measure user influence in the Github developer social network. We first preprocess the Github data, and construct the social network. Then, we analyze user influence in the social network, in terms of popularity, centrality, content value, contribution and activity. Finally, we analyze the correlation of different user influence measures, and use Borda Count to comprehensively quantify user influence and verify the results.

Identifying unusual commits on GitHub

AbstractTransparent environments and social‐coding platforms as GitHub help developers to stay abreast of changes during the development and maintenance phase of a project. Especially, notification feeds can help developers to learn about relevant changes in other projects. Unfortunately, transparent environments can quickly overwhelm developers with too many notifications, such that they lose the important ones in a sea of noise. Complementing existing prioritization and filtering strategies based on binary compatibility and code ownership, we develop an anomaly detection mechanism to identify unusual commits in a repository, which stand out with respect to other changes in the same repository or by the same developer. Among others, we detect exceptionally large commits, commits at unusual times, and commits touching rarely changed file types given the characteristics of a particular repository or developer. We automatically flag unusual commits on GitHub through a browser plug‐in. In an interactive survey with 173 active GitHub users, rating commits in a project of their interest, we found that, although our unusual score is only a weak predictor of whether developers want to be notified about a commit, information about unusual characteristics of a commit changes how developers regard commits. Our anomaly detection mechanism is a building block for scaling transparent environments.

How Social and Communication Channels Shape and Challenge a Participatory Culture in Software Development

Software developers use many different communication tools and channels in their work. The diversity of these tools has dramatically increased over the past decade and developers now have access to a wide range of socially enabled communication channels and social media to support their activities. The availability of such social tools is leading to a participatory culture of software development, where developers want to engage with, learn from, and co-create software with other developers. However, the interplay of these social channels, as well as the opportunities and challenges they may create when used together within this participatory development culture are not yet well understood. In this paper, we report on a large-scale survey conducted with 1,449 GitHub users. We discuss the channels these developers find essential to their work and gain an understanding of the challenges they face using them. Our findings lay the empirical foundation for providing recommendations to developers and tool designers on how to use and improve tools for software developers.

GitHub use in public administration in Canada: Early experience with a new collaboration tool

AbstractGitHub is a web‐based digital project hosting service that facilitates collaboration. We introduce how GitHub works and assess how it has been used in the public sector in Canada based on interviews with federal government leaders and a survey of Canadian public service GitHub users. With little research to date on public sector use of GitHub, and none on its use in Canadian governments, we explore the early experience with this collaboration tool and consider the possible implications for collaboration in government.

Influence analysis of Github repositories.

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

Understanding the popular users: Following, affiliation influence and leadership on GitHub

Context: the ability to follow other users and projects on GitHub has introduced a new layer of open source software development participants who observe but do not contribute to projects. It has not been fully explored how following others influences the actions of GitHub users. Objective: this paper studies the motivation behind following (or not following) others and the influence of popular users on their followers. Method: a mixed methods research approach was used including a survey of 800 GitHub users to uncover the reasons for following on GitHub and a complementary quantitative analysis of the activity of GitHub users to examine influence. Our quantitative analysis studied 199 popular (most followed) users and their followers. Results: we found that popular users do influence their followers by guiding them to new projects. As a user’s popularity increases, so does their rate of influence, yet the same is not true for a popular user’s rate of contribution. Conclusions: these results indicate that a new type of leadership is emerging through GitHub’s following feature and popularity can be more important than contribution in influencing others. We discuss implications of popularity and influence and their impact on social structure and leadership on OSS projects.

Coding Together at Scale: GitHub as a Collaborative Social Network

GitHub is the most popular repository for open source code. It has more than 3.5 million users, as the company declared in April 2013, and more than 10 million repositories, as of December 2013. It has a publicly accessible API and, since March 2012, it also publishes a stream of all the events occurring on public projects. Interactions among GitHub users are of a complex nature and take place in different forms. Developers create and fork repositories, push code, approve code pushed by others, bookmark their favorite projects and follow other developers to keep track of their activities.In this paper we present a characterization of GitHub, as both a social network and a collaborative platform. To the best of our knowledge, this is the first quantitative study about the interactions happening on GitHub. We analyze the logs from the service over 18 months (between March 11, 2012 and September 11, 2013), describing 183.54 million events and we obtain information about 2.19 million users and 5.68 million repositories, both growing linearly in time. We show that the distributions of the number of contributors per project, watchers per project and followers per user show a power-law-like shape. We analyze social ties and repository-mediated collaboration patterns, and we observe a remarkably low level of reciprocity of the social connections. We also measure the activity of each user in terms of authored events and we observe that very active users do not necessarily have a large number of followers. Finally, we provide a geographic characterization of the centers of activity and we investigate how distance influences collaboration.