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16th IEEE/ACM International Conference on Cooperative and Human Aspects of Software Engineering (CHASE)

作者： Ferreyra, Nicolas E. Diaz Inane, Abdessamad Vidoni, Melina Scandariato, Riccardo TU Hamburg Inst Software Secur Hamburg Germany Lorraine Univ Nancy France Loria Cnrs Inria Nancy Grand Est Nancy France Australian Natl Univ Sch Comp Canberra ACT Australia

ISBN: (纸本)9798350337341

social coding platforms (SCPs) like GitHub have become central to modern software engineering thanks to their collaborative and version-control features. Like in mainstream Online social Networks (OSNs) such as Facebook, users of SCPs are subjected to privacy attacks and threats given the high amounts of personal and project-related data available in their profiles and software repositories. However, unlike in OSNs, the privacy concerns and practices of SCP users have not been extensively explored nor documented in the current literature. In this work, we present the preliminary results of an online survey (N=105) addressing developers' concerns and perceptions about privacy threats steaming from SCPs. Our results suggest that, although users express concern about social and organisational privacy threats, they often feel safe sharing personal and project-related information on these platforms. Moreover, attacks targeting the inference of sensitive attributes are considered more likely than those seeking to re-identify source-code contributors. Based on these findings, we propose a set of recommendations for future investigations addressing privacy and identity management in SCPs.

关键词： social coding platforms privacy concerns usable security privacy engineering

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Understanding the Helpfulness of Stale Bot for Pull-Based Development: An Empirical Study of 20 Large Open-Source Projects

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第2期33卷 1-43页

作者： Khatoonabadi, Sayedhassan Costa, Diego Elias Mujahid, Suhaib Shihab, Emad Concordia Univ Dept Comp Sci & Software Engn Data Driven Anal Software DAS Lab 2155 Guy St Montreal PQ H3H 2L9 Canada Concordia Univ Dept Comp Sci & Software Engn 2155 Guy St Montreal PQ H3H 2L9 Canada Mozilla Corp Montreal PQ Canada

Pull Requests (PRs) that are neither progressed nor resolved clutter the list of PRs, making it difficult for the maintainers to manage and prioritize unresolved PRs. To automatically track, follow up, and close such inactive PRs, Stale bot was introduced by GitHub. Despite its increasing adoption, there are ongoing debates on whether using Stale bot alleviates or exacerbates the problem of inactive PRs. To better understand if and how Stale bot helps projects in their pull-based development workflow, we perform an empirical study of 20 large and popular open source projects. We find that Stale bot can help deal with a backlog of unresolved PRs, as the projects closed more PRs within the first few months of adoption. Moreover, Stale bot can help improve the efficiency of the PR review process as the projects reviewed PRs that ended up merged and resolved PRs that ended up closed faster after the adoption. However, Stale bot can also negatively affect the contributors, as the projects experienced a considerable decrease in their number of active contributors after the adoption. Therefore, relying solely on Stale bot to deal with inactive PRs may lead to decreased community engagement and an increased probability of contributor abandonment.

关键词： Software development bots pull request abandonment pull-based development modern code review social coding platforms open source software

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social NETWORK ANALYSIS AND MINING 2024年第1期14卷 146-146页

作者： Al Rubaye, Abduljaleel Sukthankar, Gita Univ Cent Florida Dept Comp Sci 4000 Cent Florida Blvd Orlando FL 32816 USA

Tech-related online communities on GitHub, Reddit, and Stack Overflow are an invaluable resource for software engineers, allowing them to find solutions to problems and connect with other professionals. Much of the discourse on these platforms is conducted using commenting mechanisms in which one user responds to content posted by another user. Even though these communities lack formal organizational structures, these technologists are often followed by other software developers who monitor their posts;users who regularly post useful solutions are recognized using platform-specific mechanisms such as stars or karma points. This article investigates the relationship between popularity and discourse in tech-related online communities. To do this, we create comment timelines from sequences of user interactions and extract commenting networks from comment response patterns. We study how the popularity of the post authors and other commenters shapes community interaction. Although there are some commonalities, there are distinct differences between the commenting behavior of GitHub users vs. Reddit and Stack Overflow. Popularity influences the length of commenting timelines on GitHub, whereas this effect isn't observed on Reddit or Stack Overflow. However on all three platforms, user seniority appears to have a stronger impact on the structure of commenting networks than popularity. By understanding how popularity affects user interactions, we can design online communities that are more effective at supporting knowledge sharing and problem solving. Our cross-platform comment dataset is available for download at: https://***/abdul-dissertation-dataset.

关键词： social coding platforms Tech-related forums Popularity Knowledge sharing

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15th IEEE/ACM Annual International Conference on Advances in social Networks Analysis and Mining (ASONAM)

作者： Al Rubaye, Abduljaleel Sukthankar, Gita Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9798400704093

Tech-related online communities on GitHub, Reddit, and Stack Overflow are an invaluable resource for software engineers, allowing them to find solutions to problems and connect with other professionals. Much of the discourse on these platforms is conducted using commenting mechanisms in which one user responds to content posted by another user. Even though these communities lack formal organizational structures, these technologists are often followed by other software developers who monitor their posts;users who regularly post useful solutions are recognized using platform-specific mechanisms such as stars or karma points. This paper investigates the relationship between popularity and discourse in tech-related online communities. To do this, we create comment timelines from sequences of user interactions and extract commenting networks from comment response patterns. Although there are some commonalities, there are distinct differences between the commenting behavior of GitHub users vs. Reddit and Stack Overflow. By understanding how popularity affects user interactions, we can design communities that are more effective at supporting learning, collaboration, and knowledge sharing.

关键词： social coding platforms tech-related forums popularity knowledge sharing

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An Exploratory Study of Reactions to Bot Comments on GitHub 4

An Exploratory Study of Reactions to Bot Comments on GitHub

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IEEE/ACM 4th International Workshop on Bots in Software Engineering (BotSE)

作者： Farah, Juan Carlos Spaenlehauer, Basile Lu, Xinyang Ingram, Sandy Gillet, Denis Ecole Polytech Fed Lausanne Lausanne Switzerland Nanyang Technol Univ Singapore Singapore Univ Appl Sci Fribourg Switzerland

ISBN: (纸本)9781665462228

The widespread use of bots to support software development makes social coding platforms such as GitHub a particularly rich source of data for the study of human-bot interaction. Software development bots are used to automate repetitive tasks, interacting with their human counterparts via comments posted on the various discussion interfaces available on such platforms. One type of interaction supported by GitHub involves reacting to comments using predefined emoji. To investigate how users react to bot comments, we conducted an observational study comprising 54 million GitHub comments, with a particular focus on comments that elicited the laugh reaction. The results from our analysis suggest that some reaction types are not equally distributed across human and bot comments and that a bot's design and purpose influence the types of reactions it receives. Furthermore, while the laugh reaction is not exclusively used to express laughter, it can be used to convey humor when a bot behaves unexpectedly. These insights could inform the way bots are designed and help developers equip them with the ability to recognize and recover from unanticipated situations. In turn, bots could better support the communication, collaboration, and productivity of teams using social coding platforms.

关键词： bots humor laugh emoji reactions social coding platforms GitHub

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Detecting and Characterizing Bots that Commit Code 17

Detecting and Characterizing Bots that Commit Code

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IEEE/ACM 17th International Conference on Mining Software Repositories (MSR)

作者： Dey, Tapajit Mousavi, Sara Ponce, Eduardo Fry, Tanner Vasilescu, Bogdan Univ Tennessee Knoxville TN 37996 USA Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9781450375177

Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality, it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the commits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of which have more than 1000 commits) and 13,762,430 commits they created.

关键词： bots automated commits random forest ensemble model social coding platforms software engineering

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On the Accuracy of Bot Detection Techniques 4

On the Accuracy of Bot Detection Techniques

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IEEE/ACM 4th International Workshop on Bots in Software Engineering (BotSE)

作者： Golzadeh, Mehdi Decan, Alexandre Chidambaram, Natarajan Univ Mons Software Engn Lab Mons Belgium

ISBN: (纸本)9781665462228

Development bots are often used to automate a wide variety of repetitive tasks in collaborative software development. Such bots are commonly among the most active project contributors in terms of commit activity. As such, tools that analyse contributor activity (e.g., for recognizing and giving credit to project members for their contributions) need to take into account the bots and exclude their activity. While there are a few techniques to detect bots in software repositories, these techniques are not perfect and may miss some bots or may wrongly identify some human accounts as bots. In this paper, we present an exploratory study on the accuracy of bot detection techniques on a set of 540 accounts from 27 GitHub projects. We show that none of the bot detection techniques are accurate enough to detect bots among the 20 most active contributors of each project. We show that combining these techniques drastically increases the accuracy and recall of bot detection. We also highlight the importance of considering bots when attributing contributions to humans, since bots are prevalent among the top contributors and responsible for large proportions of commits.

关键词： social coding platforms bot detection techniques contributor attribution empirical analysis GitHub software repository mining

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Human, bot or both? A study on the capabilities of classification models on mixed accounts 37

Human, bot or both? A study on the capabilities of classific...

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37th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Cassee, Nathan Kitsanelis, Christos Constantinou, Eleni Serebrenik, Alexander Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781665428828

Several bot detection algorithms have recently been discussed in the literature, as software bots that perform maintenance tasks have become more popular in recent years. State-of-the-art techniques detect bots based on a binary classification, where a GitHub account is either a human or a bot. However, this conceptualisation of bot detection as an account-level binary classification problem fails to account for 'mixed accounts', accounts that are shared between a human and a bot, and that therefore exhibit both bot and human activity. By using binary classification models for bot detection, researchers might hence mischaracterize both human and bot behavior in software maintenance. This calls for conceptualisation of bot detection through a comment-level classification. However, the single such approach solely investigates a small number of mixed account comments. The nature of mixed accounts on GitHub is thus yet unknown, and the absence of appropriate datasets make this a difficult problem to study. In this paper, we investigate three comment-level classification models and we evaluate these classifiers on a manually labeled dataset of mixed accounts. We find that the best classifiers based on these classification models achieve a precision and recall between 88% and 96%. However, even the most accurate comment-level classifier cannot accurately detect mixed accounts;rather, we find that textual content alone, or textual content combined with templates used by bots, are very effective features for the detection of both bot and mixed accounts. Our study calls for more accurate bot detection techniques capable of identifying mixed accounts, and as such supporting more refined insights in software maintenance activities performed by humans and bots on social coding sites.

关键词： bot identification classification model social coding platforms GitHub software engineering

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OnWasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests-A Mixed-Methods Study of 10 Large Open-Source Projects

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2023年第1期32卷 15-15页

作者： Khatoonabadi, Sayedhassan Costa, Diego Elias Abdalkareem, Rabe Shihab, Emad Concordia Univ Dept Comp Sci & Software Engn Data Driven Anal Software Lab 2155 Guy St Montreal PQ H3H 2L9 Canada Carleton Univ Sch Comp Sci 1125 Colonel By Dr Ottawa ON K1S 5B6 Canada

Pull-based development has enabled numerous volunteers to contribute to open-source projects with fewer barriers. Nevertheless, a considerable amount of pull requests (PRs) with valid contributions are abandoned by their contributors, wasting the effort and time put in by both the contributors and maintainers. To better understand the underlying dynamics of contributor-abandoned PRs, we conduct a mixed-methods study using both quantitative and qualitative methods. We curate a dataset consisting of 265,325 PRs including 4,450 abandoned ones from ten popular and mature GitHub projects and measure 16 features characterizing PRs, contributors, review processes, and projects. Using statistical and machine learning techniques, we find that complex PRs, novice contributors, and lengthy reviews have a higher probability of abandonment and the rate of PR abandonment fluctuates alongside the projects' maturity or workload. To identify why contributors abandon their PRs, we also manually examine a random sample of 354 abandoned PRs. We observe that the most frequent abandonment reasons are related to the obstacles faced by contributors, followed by the hurdles imposed by maintainers during the review process. Finally, we survey the top core maintainers of the studied projects to understand their perspectives on dealing with PR abandonment and on our findings.

关键词： Socio-technical factors pull-based development modern code review social coding platforms open-source software mixed-methods research

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Cybersecurity discussions in Stack Overflow: a developer-centred analysis of engagement and self-disclosure behaviour

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social NETWORK ANALYSIS AND MINING 2023年第1期14卷 16-16页

作者： Ferreyra, Nicolas E. Diaz Vidoni, Melina Heisel, Maritta Scandariato, Riccardo Hamburg Univ Technol Hamburg Germany Australian Natl Univ Canberra Australia Univ Duisburg Essen Duisburg Germany

Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users' engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site's relevance and reputation into jeopardy. Hence, it is important to understand users' participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users' engagement and their privacy practices in SO. Particularly, it analyses developers' self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers' engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.

关键词： Stack Overflow Usable privacy and security Engagement Self-disclosure R programming Python social coding platforms

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