Open Source Software Communities Research Articles

Organizational Influence on Security Development in Open-Source Software Projects

Increasing technological complexity, intensified competition, and security requirements have driven open-source software (OSS) projects to become a crucial part of organizations' software development. This study focuses on the OSS project TensorFlow (TF) and uses a case study to examine how organizations and their associated developers collaborate to identify, fix and prevent security vulnerabilities. Social Network Analysis (SNA) of archived security data from software repositories is used to gain insight into security activities. The study examines the internal structure and evolution of security code collaboration, organizational networks, and top organizational contributors to TF. It also examines productivity, homophily, development diversity, and turnover rates among developers across various software releases. The in-depth insights from this research enhance our understanding of collaborative patterns in OSS communities within open software ecosystems, particularly in the security context.

Free open source communities sustainability: Does it make a difference in software quality?

ContextFree and Open Source Software (FOSS) communities’ ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.ObjectiveThis study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.Method16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.ResultsFindings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.ConclusionFindings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.

Automated Commit Intelligence by Pre-training

GitHub commits, which record the code changes with natural language messages for description, play a critical role in software developers’ comprehension of software evolution. Due to their importance in software development, several learning-based works are conducted for GitHub commits, such as commit message generation and security patch identification. However, most existing works focus on customizing specialized neural networks for different tasks. Inspired by the superiority of code pre-trained models, which has confirmed their effectiveness across different downstream tasks, to promote the development of open-source software community, we first collect a large-scale commit benchmark including over 7.99 million commits across 7 programming languages. Based on this benchmark, we present CommitBART, a pre-trained encoder-decoder Transformer model for GitHub commits. The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation, and contrastive learning) for six pre-training tasks to learn commit fragment representations. Our model is evaluated on one understanding task and three generation tasks for commits. The comprehensive experiments on these tasks demonstrate that CommitBART significantly outperforms previous pre-trained works for code. Further analysis also reveals that each pre-training task enhances the model performance.

Infrastructural justice for responsible software engineering,

In recent years, we have seen many examples of software products unintentionally causing demonstrable harm. Many guidelines for ethical and responsible computing have been developed in response. Dominant approaches typically attribute liability and blame to individual companies or actors, rather than understanding how the working practices, norms, and cultural understandings in the software industry contribute to such outcomes. In this paper, we propose an understanding of responsibility that is infrastructural, relational, and cultural; thus, providing a foundation to better enable responsible software engineering into the future. Our approach draws on Young's (2006) social connection model of responsibility and Star and Ruhleder's (1994) concept of infrastructure. By bringing these theories together we introduce a concept called infrastructural injustice, which offers a new way for software engineers to consider their opportunities for responsible action with respect to society and the planet. We illustrate the utility of this approach by applying it to an Open-Source software communities’ development of Deepfake technology, to find key leverage points of responsibility that are relevant to both Deepfake technology and software engineering more broadly.

Characterizing Usability Issue Discussions in Open Source Software Projects

Usability is a crucial factor but one of the most neglected concerns in open source software (OSS). While far from an ideal approach, a common practice that OSS communities adopt to collaboratively address usability is through discussions on issue tracking systems (ITSs). However, there is little knowledge about the extent to which OSS community members engage in usability issue discussions, the aspects of usability they frequently target, and the characteristics of their collaboration around usability issue discussions. This knowledge is important for providing practical recommendations and research directions to better support OSS communities in addressing this important topic and improve OSS usability in general. To help achieve this goal, we performed an extensive empirical study on issues discussed in five popular OSS applications: three data science notebook projects (Jupyter Lab, Google Colab, and CoCalc) and two code editor projects (VSCode and Atom). Our results indicated that while usability issues are extensively discussed in the OSS projects, their scope tended to be limited to efficiency and aesthetics. Additionally, these issues are more frequently posted by experienced community members and display distinguishable characteristics, such as involving more visual communication and more participants. Our results provide important implications that can inform the OSS practitioners to better engage the community in usability issue discussion and shed light on future research efforts toward collaboration techniques and tools for discussing niche topics in diverse communities, such as the usability issues in the OSS context.

Reporting on artificial intelligence use in entrepreneurship research: Using a model card

The study of artificial intelligence is of increasing importance in the entrepreneurial domain. Despite the popularity of many artificial intelligence models, experimental studies in entrepreneurship that apply models are subject to replicability issues if they are not properly reported on. This note is a call to adopt a method of reporting on artificial intelligence models commonly used in the open source software community to ensure progress in future studies and to offer researchers a reflective opportunity to consider the appropriateness of models they use in experimental studies.

The Entrainment of Task Allocation and Release Cycles in Open Source Software Development

In this study we identify a process of “entrainment” around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS.

Addressing durability in collaborative organising: Event atmospheres and polyrhythmic affectivity

Collaborative organising is known to burn like a rocket: it thrives on intense passion, relationality and creativity but quickly falls into pieces. This article explores the underestimated role of events and their affective atmospheres to sustain collaborative work. Drawing insights from two ethnographic field studies within an open-source software community and a network of impact entrepreneurs, we introduce the notion of ‘polyrhythmic affectivity’ at the core of polycentric governance. It encapsulates how frictional reverberances between three atmospherically experienced affective intensities – togetherness, dissonance and mutuality – are able to maintain emergent yet enduring order. We argue that the collective motivational force of collaborative organising, can be stabilised through a process of ‘affective commoning’ to sustain collaborative atmospheres as shared creative resources.

Discursive Modulation in Open Source Software: How Online Communities Shape Novelty and Complexity

We study the development of two open source software (OSS) web frameworks to understand how OSS communities shape software novelty and complexity in the absence of strong organizational hierarchies. We examine how projects engage in distinct “discursive modulation practices” to imprint the community’s shared core doctrines and design principles onto the software thereby shaping its novelty and complexity. We borrow the concept of modulation from audio synthesis to explain how a pre-existing signal—in our case, the ongoing community discourse—gets modulated to produce varying sounds—in our case the novelty and complexity of the software. The concept of modulation offers a lens to understand how emergent, community-wide development activities are influenced by filtering of discursive positions and mixing of those positions, thereby shaping the artifact’s novelty and complexity. Our research shows that the modulation of novelty exhibits a range from “proximal” to “distal” searches for new features, while the modulation of complexity varies between “integration” and “deprecation.” By drawing on these concepts, we formulate a theory that explains how modulation results in alternative OSS community approaches to shaping software novelty and complexity, and how this process reflects, and is reflected in, the resulting software artifact.

Value-driven industry transformation: Leveraging open-source to establish new standards

Traditional industries face growing pressure to digitally transform. In particular, established organizations must adopt new mindsets and act to remain competitive. To counter the challenges posed by monopolies and new entrants in a diverse and fiercely competitive landscape like Logistics, the Open Logistics Foundation (OLF) plays a pivotal role. It aims at fostering the development of logistics-specific open-source-software (OSS) to establish new digital services setting industry standards and ensuring enhanced connectivity and compatibility. The OLF represents an expanding core-community of dedicated logistics organizations. This core-community collaboratively identifies, selects, and develops relevant OSS for upcoming value-serving digital services and solutions. What sets this approach apart is its departure from the traditional solution-oriented open-source model. Instead of concluding with an OSS community, it starts with a dedicated core-community aiming at setting new widely adopted standards. However, even with this standard-driven approach, designing the right solution can be as challenging as finding a unicorn. To enhance the likelihood of success, this paper designed and evaluated the Minimum Viable core-Community driven Open-Source method (MV-COS). The MV-COS offers a comprehensive and standardized support allowing to holistically craft the vision, stakeholder-specific value propositions, and the corresponding implementation strategy of OSS projects. It prioritizes the early design and assessment of its likelihood to attract and align a critical mass of users which is the fundamental requirement for establishing new de-facto standards. The MV-COS enables to fail fast and iterate early, avoiding the unnecessary expenditure of resources.

The Factors Affecting User Experience Maturity in Free and Open Source Software Community: An Empirical Study

Assessing User eXperience (UX) maturity is mandatory in the free and open-source software (FOSS) community to avoid wasting resources on projects that may fall short of expectations. Best practices suggest employing UX Capability Maturity Models (UXCMMs), which often specify an evolutionary plateau toward developing a UX-matured system and quantify organizational UX maturity into maturity levels ranging from no or ad hoc improvement to integrated continuous improvement. Numerous generically developed UXCMMs exist. However, none is created for the FOSS community, and less information is available to support their perfect match. Thus, raise suspicions if they have proper UX maturity influencing factors (UXMIFs) for the FOSS community or measure the right thing. The FOSS community differs from traditional software-developing communities and may have different dynamics and UXMIFs, necessitating unique and new methodologies for their discovery. This study, therefore, aims to identify the FOSS community’s UXMIFs and evaluate the community’s knowledge of UX maturity-related concepts. Its findings may help future researchers, practitioners, and other FOSS developers to develop UXCMMs exclusive to the FOSS community. The study’s design is both qualitative and quantitative. It adopted a systematic literature review, interviews, fuzzy Delphi Method, and thematic analysis to collect and analyze data and present the findings. The study’s sample included sixty-two active FOSS projects, fifteen FOSS stakeholders, and twelve UX experts. The outcome shows that 84% of UX experts agreed on the thirty-six FOSS’s UXMIFs with threshold d = 0.143 and crisp values greater than α-cut = 0.5. User feedback and adopted technologies were ranked first, while learnability and use speed were ranked last. Similarly, FOSS stakeholders have shown a shared understanding of UX maturity, connected concepts, and impacting factors.

Knowledge sharing in open-source software development communities: a review and synthesis

Purpose The purpose of this study is to examine the state of the literature on knowledge sharing in open source software (OSS) development communities by examining the existing research and identifying the knowledge gaps and opportunities that can inform areas for future research. Design/methodology/approach A systematic literature review was conducted of literature published between January 2011 and February 2023. A total of 24 papers were identified and reviewed. Findings The findings reveal that the literature on knowledge sharing in OSS development communities from developing countries are limited. Additionally, there exists a limited focus on the development of frameworks to support knowledge sharing in OSS communities. The transient nature of OSS development contributors’ results in knowledge loss; thus, knowledge retention needs further investigation. Research limitations/implications This study only included papers whose titles, keywords or abstracts included the search keywords “knowledge sharing” and “Open Source Software”. While the keywords were carefully applied, when applying the search, it cannot be ruled that some relevant studies might have been missed. The study was also limited to conferences and journal papers published in English. Despite the limitations, the study provides a systematic review of knowledge sharing in OSS communities and presents findings that can be useful to researchers and practitioners interested in this area. Originality/value The study provides a systematic literature review of published papers and identifies themes and future research areas on knowledge sharing in OSS communities. Additionally, this review offers insights into future research avenues for theory, content and context on knowledge sharing in OSS development communities.

From Feminist Servers to Feminist Federation

Situated within the technofeminist care practices of feminist servers, this text explores the possibilities of feminist federation. Speaking from our collective practice of system administration, we start by introducing Systerserver, laying out the feminist pedagogies that inform our practice of learning and doing together with technologies and the politics of maintenance and care. We then revisit the identity politics of feminist servers as more than safe/r spaces in the cis-male-dominated domain of free/libre and open source software communities. Finally, we reflect on our experiences of building and federating a feminist video platform with the PeerTube software on Systerserver. Facing the techno-social challenges around the protocol of federation and adapting the software alongside our federating practice, we focus on sustainable and care-oriented alternatives to ‘scaling up’ the affective infrastructures of our feminist servers.

The effect of perceived justice on users’ contribution in open source software communities

Users’ voluntary contribution of codes is crucial to the success of open source software (OSS) communities. However, users often lack the contribution motivation. The purpose of this research is to examine users’ contribution in OSS communities from a perceived justice perspective. We adopted a mixed method of structural equation modeling (SEM) and artificial neural network (ANN) to conduct data analysis. The results indicated that perceived justice, which includes distributive justice, procedural justice and interactional justice, has significant effects on trust and satisfaction, both of which further determine contribution intention. The results imply that OSS communities need to improve users’ perceived justice in order to facilitate their contribution behavior.

The Nodes Influence Maximization in Open Source Software Community Based on Probability Propagation Model

With the continuous development of Internet technology, open source software is more and more popular. Users and their interactions in a specific network plat constitute an open source software community, which can be represented by a complex network. It is of great significance to effectively and quickly identify the most influential users in the community, such as better guiding the management and development of the community, accelerating the information dissemination and personalized recommendation. Traditional methods of measuring node influence only consider some topology characteristics of the network, so the results are one-sided. Because there are different interactive behaviors among users in the community, it is necessary to mine more interaction information, so as to give a more comprehensive measure of node influence. In view of this, this paper proposes a method to maximize the node influence of open source software community based on a probability propagation model. Firstly, according to the relationship of users and their interactive records on projects, this paper quantifies the feedback of users on the project from three aspects (i.e., approve, save and modify) and establishes a new probability propagation model between users. Secondly, this paper proposes an algorithm(SIUF) to evaluate users' influence in the open source software community based on the probability propagation model. The algorithm fully considers the interaction behavior of users in the community. In the first stage, the user's own activity is taken as the initial ranking. In the second stage, the influence of neighbor nodes is taken into account, and the SIUF value of nodes is accumulated while the rich club effect is weakened. Finally, the proposed theory and method are applied to GitHub, a typical open source software community, and the correctness and effectiveness of this method are verified from influence spread and speed.

A Code Reviewer Recommendation Approach Based on Attentive Neighbor Embedding Propagation

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly.

Understanding Mentors’ Engagement in OSS Communities via Google Summer of Code

A constant influx of newcomers is essential for the sustainability and success of open source software (OSS) projects. However, successful onboarding is always challenging because newcomers face various initial contributing barriers. To support newcomer onboarding, OSS communities widely adopt the mentoring approach. Despite its significance, previous mentoring studies tend to focus on the newcomer’s perspective, leaving the mentor’s perspective relatively under-studied. To better support mentoring, we study the popular Google Summer of Code (GSoC). It is a well-established global program that offers stipends and mentors to students aiming to bring more student developers into OSS development. We combine online data analysis, an email survey, and semi-structured interviews with the GSoC mentors to understand their motivations, challenges, strategies, and gains. We propose a taxonomy of GSoC mentors’ engagement with four themes, ten categories, 34 sub-categories, and 118 codes, as well as the mentors’ attitudes toward the codes. In particular, we find that mentors participating in GSoC are primarily intrinsically motivated, and some new motivators emerge adapting to the contemporary challenges, e.g., sustainability and advertisement of projects. Forty-one challenges and 52 strategies associated with the program timeline are identified, most of which are first time revealed. Although almost all the challenges are agreed upon by specific mentors, some mentors believe that several challenges are reasonable and even have a positive effect. For example, the cognitive differences between mentors and mentees can stimulate new perspectives. Most of the mentors agreed that they had adopted these strategies during the mentoring process, but a few strategies recommended by the GSoC administration were not agreed upon. Self-satisfaction, different skills, and peer recognition are the main gains of mentors to participate in GSoC. Eventually, we discuss practical implications for mentors, students, OSS communities, GSoC programs, and researchers.

Hyperloop transportation technologies: practices for open organizing across VUCA contexts

Open organizations are structures in which members of the public engage in work for the organization. Examples include open-source software, Amnesty International, Wikipedia, and Lego communities. Much research focuses on structural design characteristics of open organizations, such as pre-specified task divisions and integration teams. These practices require the organization to a priori structure in response to its mission. Increasingly, however, open organizations like CrowdDoing and Hyperloop Transportation Technologies (HyperloopTT) require public involvement across volatile, uncertain, complex, ambiguous (VUCA) contexts. These open organizations must respond to changing political, competitive, and socio-economic events. Structural clarity is more difficult, and contributors may participate in the creative development of new technologies, new policies, and new sources of funding. Working from practices supporting participant engagement in more stable environments, we qualitatively observe HyperloopTT to understand internal practices for open organizing in more VUCA contexts. We observe four practices allowing for the flexibility, versatility, and accommodations needed for open organizing in such settings. The HyperloopTT practices allow more porosity and self-determination—not simply in how people divide and integrate tasks, but also in the exploration and experimentation of the work itself. More than task workers, we see a new class of open organizing participants: creative work designers.

An empirical examination of newcomer contribution costs in established OSS communities: a knowledge-based perspective

PurposeTo remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.Design/methodology/approachEmploying longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.FindingsThe results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.Originality/valueThis paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy

We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in the open-source software community in the sense that (i) it is consistent with NumPy arrays in indexing and slicing and; (ii) together with the rich Python libraries for machine learning, data analysis, and visualization, it is easy to implement data-driven models; and (iii) it provides convenient interfaces for users to switch and tune parameters among different solvers. History: Ted Ralphs, Area Editor for Software Tools. Funding: The research of Z. Chen is funded by the Strategic Research Grant [Project 7005792] from the City University of Hong Kong. The research of P. Xiong is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Grant MOE-2019-T3-1-010]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1291 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0146 ) at ( http://dx.doi.org/10.5281/zenodo.7463845 ).