International audience—Software development projects face a lot of risks (requirements inflation, poor scheduling, technical problems, etc.). Underestimating those risks may put in danger the project success. One of the most critical risks is the employee turnover, that is the risk of key personnel leaving the project. A good indicator to evaluate this risk is to measure the concentration of information in individual developers. This is also popularly known as the bus factor (" number of key developers who would need to be incapacitated, i.e. hit by a bus, to make a project unable to proceed "). Despite the simplicity of the concept, calculating the actual bus factor for specific projects can quickly turn into an error-prone and time-consuming activity as soon as the size of the project and development team increase. In order to help project managers to assess the bus factor of their projects, in this paper we present a tool that, given a Git-based repository, automatically measures the bus factor for any file, directory and branch in the repository and for the project itself. You can also simulate with the tool what would happen to the project (e.g., which files would become orphans) if one or more developers disappeared
Reporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. This feedback is generally reported in the form of new issues for the project, managed by the so-called issue-trackers. One of the features provided by most issue-trackers is the possibility to define a set of labels/tags to classify the issues and, at least in theory, facilitate their management. Nevertheless, there is little empirical evidence to confirm that taking the time to categorize new issues has indeed a beneficial impact on the project evolution. In this paper we analyze a population of more than three million of GitHub projects and give some insights on how labels are used in them. Our preliminary results reveal that, even if the label mechanism is scarcely used, using labels favors the resolution of issues. Our analysis also suggests that not all projects use labels in the same way (e.g., for some labels are only a way to prioritize the project while others use them to signal their temporal evolution as they move along in the development workflow). Further research is needed to precisely characterize these label "families" and learn more the ideal application scenarios for each of them.
The development Open Source Software fundamentally depends on the participation and commitment of volunteer developers to progress on a particular task. Several works have presented strategies to increase the on-boarding and engagement of new contributors, but little is known on how these diverse groups of developers self-organise to work together. To understand this, one must consider that, on one hand, platforms like GitHub provide a virtually unlimited development framework: any number of actors can potentially join to contribute in a decentralised, distributed, remote, and asynchronous manner. On the other, however, it seems reasonable that some sort of hierarchy and division of labour must be in place to meet human biological and cognitive limits, and also to achieve some level of efficiency. These latter features (hierarchy and division of labour) should translate into detectable structural arrangements when projects are represented as developer-file bipartite networks. Thus, in this paper we analyse a set of popular open source projects from GitHub, placing the accent on three key properties: nestedness, modularity and in-block nestedness –which typify the emergence of heterogeneities among contributors, the emergence of subgroups of developers working on specific subgroups of files, and a mixture of the two previous, respectively. These analyses show that indeed projects evolve into internally organised blocks. Furthermore, the distribution of sizes of such blocks is bounded, connecting our results to the celebrated Dunbar number both in off- and on-line environments. Our conclusions create a link between bio-cognitive constraints, group formation and online working environments, opening up a rich scenario for future research on (online) work team assembly (e.g. size, composition, and formation). From a complex network perspective, our results pave the way for the study of time-resolved datasets, and the design of suitable models that can mimic the growth and evolution of OSS projects.
As cloud computing allows improving the quality of software and aims at reducing costs of operating software, more and more software is delivered as a service. However, moving from a software as a product strategy to delivering software as a service hosted in cloud environments is very ambitious. This is due to the fact that managing software modernization is still a major challenge; especially when paradigm shifts, such as moving to cloud environments, are targeted that imply fundamental changes to how software is modernized, delivered, and sold. Thus, in addition to technical aspects, business aspects need also to be considered. ARTIST proposes a comprehensive software modernization approach covering business and technical aspects. In particular, ARTIST employs Model-Driven Engineering (MDE) techniques to automate the reverse engineering of legacy software and forward engineering of cloud-based software in a way that modernized software truly benefits from targeted cloud environments. Therewith, ARTIST aims at reducing the risks, time, and costs of software modernization and lowers the barriers to exploit cloud computing capabilities and new business models.
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