Open source is experiencing a renaissance period, due to the appearance of modern platforms and workflows for developing and maintaining public code. As a result, developers are creating open source software at speeds never seen before. Consequently, these projects are also facing unprecedented mortality rates. To better understand the reasons for the failure of modern open source projects, this paper describes the results of a survey with the maintainers of 104 popular GitHub systems that have been deprecated. We provide a set of nine reasons for the failure of these open source projects. We also show that some maintenance practices---specifically the adoption of contributing guidelines and continuous integration---have an important association with a project failure or success. Finally, we discuss and reveal the principal strategies developers have tried to overcome the failure of the studied projects.
 Motivation: To survive and succeed, FLOSS projects need contributors able to accomplish critical project tasks. However, such tasks require extensive project experience of long term contributors (LTCs). Aim: We measure, understand, and predict how the newcomers’ involvement and environment in the issue tracking system (ITS) affect their odds of becoming an LTC. Method: ITS data of Mozilla and Gnome, literature, interviews, and online documents were used to design measures of involvement and environment. A logistic regression model was used to explain and predict contributor’s odds of becoming an LTC. We also reproduced the results on new data provided by Mozilla. Results: We constructed nine measures of involvement and environment based on events recorded in an ITS. Macro-climate is the overall project environment while micro-climate is person-specific and varies among the participants. Newcomers who are able to get at least one issue reported in the first month to be fixed, doubled their odds of becoming an LTC. The macro-climate with high project popularity and the micro-climate with low attention from peers reduced the odds. The precision of LTC prediction was 38 times higher than for a random predictor. We were able to reproduce the results with new Mozilla data without losing the significance or predictive power of the previously published model. We encountered unexpected changes in some attributes and suggest ways to make analysis of ITS data more reproducible. Conclusions: The findings suggest the importance of initial behaviors and experiences of new participants and outline empirically-based approaches to help the communities with the recruitment of contributors for long-term participation and to help the participants contribute more effectively. To facilitate the reproduction of the study and of the proposed measures in other contexts, we provide the data we retrieved and the scripts we wrote at https: www.passion-lab.org projects developerfluency.html.  The advent of distributed version control systems has led to the development of a new paradigm for distributed software development; instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull-based development by offering workflow support tools, such as code reviewing systems and integrated issue trackers. In this work, we explore how pull-based software development works, first on the GHTorrent corpus and then on a carefully selected sample of 291 projects. We find that the pull request model offers fast turnaround, increased opportunities for community engagement and decreased time to incorporate contributions. We show that a relatively small number of factors affect both the decision to merge a pull request and the time to process it. We also examine the reasons for pull request rejection and find that technical ones are only a small minority. Forking is the creation of a new software repository by copying another repository. Though forking is controversial in traditional open source software (OSS) community, it is encouraged and is a built-in feature in GitHub. Developers freely fork repositories, use codes as their own and make changes. A deep understanding of repository forking can provide important insights for OSS community and GitHub. In this paper, we explore why and how developers fork what from whom in GitHub. We collect a dataset containing 236,344 developers and 1,841,324 forks. We make surveys, and analyze programming languages and owners of forked repositories. Our main observations are: (1) Developers fork repositories to submit pull requests, fix bugs, add new features and keep copies etc. Developers find repositories to fork from various sources: search engines, external sites (e.g., Twitter, Reddit), social relationships, etc. More than 42 of developers that we have surveyed agree that an automated recommendation tool is useful to help them pick repositories to fork, while more than 44.4 of developers do not value a recommendation tool. Developers care about repository owners when they fork repositories. (2) A repository written in a developer's preferred programming language is more likely to be forked. (3) Developers mostly fork repositories from creators. In comparison with unattractive repository owners, attractive repository owners have higher percentage of organizations, more followers and earlier registration in GitHub. Our results show that forking is mainly used for making contributions of original repositories, and it is beneficial for OSS community. Moreover, our results show the value of recommendation and provide important insights for GitHub to recommend repositories. Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which could help them on building and evolving systems in a competitive software market.    According to its proponents, open source style software development has the capacity to compete successfully, and perhaps in many cases displace, traditional commercial development methods. In order to begin investigating such claims, we examine data from two major open source projects, the Apache web server and the Mozilla browser. By using email archives of source code change history and problem reports we quantify aspects of developer participation, core team size, code ownership, productivity, defect density, and problem resolution intervals for these OSS projects. We develop several hypotheses by comparing the Apache project with several commercial projects. We then test and refine several of these hypotheses, based on an analysis of Mozilla data. We conclude with thoughts about the prospects for high-performance commercial open source process hybrids.  