Abstract-Online communities are flourishing as social meeting web-spaces for users and peer community members. Different online communities require different levels of competence for participants to join, and scattered evidence suggests that women can be overly under-represented. Moreover, anecdotal evidence of the Q&A website StackOverflow suggests that women withdraw from unfriendly online communities.Due to the lack of empirical evidence on the matter, this paper provides a quantitative study of the phenomenon, in order to assess the representation and social impact of gender in StackOverflow. This study positions itself within recent and focused international initiatives, launched by the European Commission in order to encourage women in the field of sciences and technology. Our findings confirm that men represent the vast majority of contributors to StackOverflow. Moreover, men participate more, earn more reputation, and engage in the "game" more than women do.
There are some concerns in the research community about the convenience of using low-level metrics (such as SLOC, source lines of code) for characterizing the evolution of software, instead of the more traditional higher lever metrics (such as the number of modules or files). This issue has been raised in particular after some studies that suggest that libre (free, open source) software evolves differently than 'traditional' software, and therefore it does not conform to Lehman's laws of software evolution. Since those studies on libre software evolution use SLOCs as the base metric, while Lehman's and other traditional studies use modules or files, it is difficult to compare both cases. To overcome this difficulty, and to explore the differences between SLOC and files/modules counts in libre software projects, we have selected a large sample of programs and have calculated both size metrics over time. Our study shows that in those cases the evolution patterns in both cases (counting SLOCs or files) is the same, and that some patterns not conforming to Lehman's laws are indeed apparent.
Some free software and open source projects have been extremely successful in the past. The success of a project is often related to the number of developers it can attract: a larger community of developers (the `bazaar') identifies and corrects more software defects and adds more features via a peer-review process. In this paper two free software projects (Wine and Arla) are empirically explored in order to characterize their software lifecycle, development processes and communities. Both the projects show a phase where the number of active developers and the actual work performed on the system is constant, or does not grow: we argued that this phase corresponds to the one termed 'cathedral' in the literature. One of the two projects (Wine) shows also a second phase: a sudden growing amount of developers corresponds to a similar growing output produced: we termed this as the `bazaar' phase, and we also argued that this phase was not achieved for the other system. A further analysis revealed that the transition between `cathedral' and `bazaar' was a phase by itself in Wine, achieved by creating a growing amount of new modules, which attracted new developers.
Because of the distributed and collaborative nature of free / open source software (FOSS) projects, the development effort invested in a project is usually unknown, even after the software has been released. However, this information is becoming of major interest, especially -but not onlybecause of the growth in the number of companies for which FOSS has become relevant for their business strategy. In this paper we present a novel approach to estimate effort by considering data from source code management repositories. We apply our model to the OpenStack project, a FOSS project with more than 1,000 authors, in which several tens of companies cooperate. Based on data from its repositories and together with the input from a survey answered by more than 100 developers, we show that the model offers a simple, but sound way of obtaining software development estimations with bounded margins of error.
Software evolution and maintenance is largely based on data gathered through years of experience: understanding and improving software is often a matter of how much data is available. Open Source software offers the opportunity to analyze closely all the phases in the evolution of a project. What’s more, data regarding its evolution is generally available for inspections. Based on simply code analyses, lots of questions about its efficiencies can’t be resolved. It would be necessary to study the process from the inside, understanding who or what drove what improvement and so on. Still a quantitative analysis gives several insights about how much code is created and evolved by developers. This study takes a sample of 12 open source projects and gives some statistics to analyze their evolution. The purpose is here to compare what is commonly know in software evolution in traditional environments, and what happens instead in open environments
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