Identifying Unmaintained Projects in GitHub

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.Comment: Accepted at 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), 10 pages, 201

Virtual Organizational Learnign in Open Source Software Development Projects

We studied the existence of virtual organizational learning in open source software (OSS) development projects. Specifically, our research focused on learning effects of OSS projects and factors that affect the learning process. The number and percentage of resolved bugs and bug resolution time of 118 SourceForge.net OSS projects were used to measure the learning effects> Projects were characterized by project type, number and experience of developers, number of bugs, and bug resolution time. Our results provide evidence of virtual organizational learning in OSS development projects.Virtual organizational leraning: Organizational learning curve: Virtual organization: Open source software development: Project performance

Skills, Division of Labor and Performance in Collective Inventions. Evidence from the Open Source Software

This paper investigates the role of skills and the division of labor among participants in collective inventions. Our analysis draws on a large sample of projects registered at Sourceforge.net, the world’s largest incubator of open source software activity. We explore the hypothesis that the level of skills of participants and their skill variety are important for project performance. Skill heterogeneity across participants is in line with two fundamental organizational features of the open source development model: team work and modular design. We also test the hypothesis whether the level of modularization of project activities is an important predictor of performance. The results provide support to the hypothesis that the skill level is important for the survival of open source projects. Moreover, we found that skill heterogeneity is positive for innovation. Finally, design modularity is positively associated with the performance of the project.Software, Technological innovation, Human capital, Modularity

Collaborative Development within Open Source Communities

Open source communities are one of the most successful-- and least appreciated--examples of high-performance collaboration and community building on the Internet today. Open source communities began as loosely organized, ad-hoc communities of contributors from all over the world who shared an interest in meeting a common need. However, the organization of these communities has proven to be very flexible and capable of carrying out all kind of developments, ranging from minor projects to huge programs such as Apache (Höhn, & Herr, 2004; Mockus, Fielding, & Herbsleb, 2005

Skills, Division of Labor and Performance in Collective Inventions. Evidence from the Open Source Software

This paper investigates the role of skills and the division of labor among participants in collective inventions. Our analysis draws on a large sample of projects registered at Sourceforge.net, the world’s largest incubator of open source software activity. We explore the hypothesis that the level of skills of participants and their skill variety are important for project performance. Skill heterogeneity across participants is in line with two fundamental organizational features of the open source development model: team work and modular design. We also test the hypothesis whether the level of modularization of project activities is an important predictor of performance. The results provide support to the hypothesis that the skill level is important for the survival of open source projects. Moreover, we found that skill heterogeneity is positive for innovation. Finally, design modularity is positively associated with the performance of the project.This paper investigates the role of skills and the division of labor among participants in collective inventions. Our analysis draws on a large sample of projects registered at Sourceforge.net, the world’s largest incubator of open source software activity. We explore the hypothesis that the level of skills of participants and their skill variety are important for project performance. Skill heterogeneity across participants is in line with two fundamental organizational features of the open source development model: team work and modular design. We also test the hypothesis whether the level of modularization of project activities is an important predictor of performance. The results provide support to the hypothesis that the skill level is important for the survival of open source projects. Moreover, we found that skill heterogeneity is positive for innovation. Finally, design modularity is positively associated with the performance of the project.Refereed Working Papers / of international relevanc

Design Architecture, Developer Networks and Performance of Open Source Software Projects

In this study we seek to understand the factors differentiating successful from unsuccessful software projects. This article develops and tests a model measuring the impact on software project performance of (1) software products ’ design architectures and (2) developers ’ positions within collaborative networks. Two indicators of project success are used: product quality and project velocity. Two dimensions of design architecture – degree of decomposition and coupling – and one characteristic of developer network structures – degree centrality – are investigated for their impact on project performance. Using data gathered from SourceForge.net and its monthly dumps, we empirically test hypotheses on the top 100 projects according to project rankings. These rankings are generated from the traffic, communication, and development statistics collected for each project hosted on SourceForge.net. Besides the top 100 projects, we also randomly choose another 100 projects to form the data sample. The main findings are that (1) the degree of decomposition has an inverted U-shaped relationship with project performance, (2) when tested on the sample of top 100 projects, average degree centrality of a project team has a positive and significant effect on project performance and (3) the effects of network metrics o

Motivation and Sorting in Open Source Software Innovation

This paper studies the role of intrinsic motivation, reputation, and reciprocity in driving open source software innovation. Unlike previous literature based on survey data, we exploit the observed pattern of contributions - the .revealed preference. of developers - to infer the underlying incentives driving the decision to contribute source code. Using detailed information on code contributions and project membership, we classify software developers into distinct types and study how contributions from each developer type vary according to the open source license type and other project characteristics. We find that developers strongly sort by license type, project size, and corporate sponsorship, and that reciprocity is important only for a small subset of projects. We also show that contributions have a substantial impact on the performance of open source projects.open source software, innovation, incentives, intrinsic motivation, motivated agents, reputation, reciprocity

An Event-based Analysis Framework for Open Source Software Development Projects

The increasing popularity and success of Open Source Software (OSS) development projects has drawn significant attention of academics and open source participants over the last two decades. As one of the key areas in OSS research, assessing and predicting OSS performance is of great value to both OSS communities and organizations who are interested in investing in OSS projects. Most existing research, however, has considered OSS project performance as the outcome of static cross-sectional factors such as number of developers, project activity level, and license choice. While variance studies can identify some predictors of project outcomes, they tend to neglect the actual process of development. Without a closer examination of how events occur, an understanding of OSS projects is incomplete. This dissertation aims to combine both process and variance strategy, to investigate how OSS projects change over time through their development processes; and to explore how these changes affect project performance. I design, instantiate, and evaluate a framework and an artifact, EventMiner, to analyze OSS projects’ evolution through development activities. This framework integrates concepts from various theories such as distributed cognition (DCog) and complexity theory, applying data mining techniques such as decision trees, motif analysis, and hidden Markov modeling to automatically analyze and interpret the trace data of 103 OSS projects from an open source repository. The results support the construction of process theories on OSS development. The study contributes to literature in DCog, design routines, OSS development, and OSS performance. The resulting framework allows OSS researchers who are interested in OSS development processes to share and reuse data and data analysis processes in an open-source manner

How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?

Although the open source model bears many advantages in software development, open source projects are always hard to sustain. Previous research on open source sustainability mainly focuses on projects that have already reached a certain level of maturity (e.g., with communities, releases, and downstream projects). However, limited attention is paid to the development of (sustainable) open source projects in their infancy, and we believe an understanding of early sustainability determinants is crucial for project initiators, incubators, newcomers, and users. In this paper, we aim to explore the relationship between early participation factors and long-term project sustainability. We leverage a novel methodology combining the Blumberg model of performance and machine learning to predict the sustainability of 290,255 GitHub projects. Specificially, we train an XGBoost model based on early participation (first three months of activity) in 290,255 GitHub projects and we interpret the model using LIME. We quantitatively show that early participants have a positive effect on project's future sustained activity if they have prior experience in OSS project incubation and demonstrate concentrated focus and steady commitment. Participation from non-code contributors and detailed contribution documentation also promote project's sustained activity. Compared with individual projects, building a community that consists of more experienced core developers and more active peripheral developers is important for organizational projects. This study provides unique insights into the incubation and recognition of sustainable open source projects, and our interpretable prediction approach can also offer guidance to open source project initiators and newcomers.Comment: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023