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.
Recent research on open source has focused on the organization of successful open source proj -ects @cite_23 , on how to attract and retain newcomers @cite_18 @cite_6 @cite_34 @cite_36 @cite_35 , and on specific features provided by GitHub, such as pull requests @cite_33 @cite_0 @cite_4 , forks @cite_22 , and stars @cite_7 @cite_15 .
@cite_35 @cite_18 @cite_4 @cite_33 @cite_22 @cite_7 @cite_36 @cite_6 @cite_0 @cite_23 @cite_15 @cite_34
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Display Omitted ContextNumerous open source software projects are based on volunteers collaboration and require a continuous influx of newcomers for their continuity. Newcomers face barriers that can lead them to give up. These barriers hinder both developers willing to make a single contribution and those willing to become a project member. ObjectiveThis study aims to identify and classify the barriers that newcomers face when contributing to open source software projects. MethodWe conducted a systematic literature review of papers reporting empirical evidence regarding the barriers that newcomers face when contributing to open source software (OSS) projects. We retrieved 291 studies by querying 4 digital libraries. Twenty studies were identified as primary. We performed a backward snowballing approach, and searched for other papers published by the authors of the selected papers to identify potential studies. Then, we used a coding approach inspired by open coding and axial coding procedures from Grounded Theory to categorize the barriers reported by the selected studies. ResultsWe identified 20 studies providing empirical evidence of barriers faced by newcomers to OSS projects while making a contribution. From the analysis, we identified 15 different barriers, which we grouped into five categories: social interaction, newcomers' previous knowledge, finding a way to start, documentation, and technical hurdles. We also classified the problems with regard to their origin: newcomers, community, or product. ConclusionThe results are useful to researchers and OSS practitioners willing to investigate or to implement tools to support newcomers. We mapped technical and non-technical barriers that hinder newcomers' first contributions. The most evidenced barriers are related to socialization, appearing in 75 (15 out of 20) of the studies analyzed, with a high focus on interactions in mailing lists (receiving answers and socialization with other members). There is a lack of in-depth studies on technical issues, such as code issues. We also noticed that the majority of the studies relied on historical data gathered from software repositories and that there was a lack of experiments and qualitative studies in this area. 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. Given the increasing number of unsuccessful pull requests in GitHub projects, insights into the success and failure of these requests are essential for the developers. In this paper, we provide a comparative study between successful and unsuccessful pull requests made to 78 GitHub base projects by 20,142 developers from 103,192 forked projects. In the study, we analyze pull request discussion texts, project specific information (e.g., domain, maturity), and developer specific information (e.g., experience) in order to report useful insights, and use them to contrast between successful and unsuccessful pull requests. We believe our study will help developers overcome the issues with pull requests in GitHub, and project administrators with informed decision making. Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open source developments are burdened by the rate at which new bug reports appear in the bug repository. In this paper, we present a semi-automated approach intended to ease one part of this process, the assignment of reports to a developer. Our approach applies a machine learning algorithm to the open bug repository to learn the kinds of reports each developer resolves. When a new report arrives, the classifier produced by the machine learning technique suggests a small number of developers suitable to resolve the report. With this approach, we have reached precision levels of 57 and 64 on the Eclipse and Firefox development projects respectively. We have also applied our approach to the gcc open source development with less positive results. We describe the conditions under which the approach is applicable and also report on the lessons we learned about applying machine learning to repositories used in open source development.