Software systems intensively depend on external libraries, chosen at conception time. However, relevance of any library irremediably changes during projects and/or library life cycle. As a consequence, projects developers must periodically reconsider the libraries they depend on, and must think about library migration. When they want to migrate their libraries, they then have to identify candidate libraries that offer similar facilities and thus can substitute to each other. They also have to compare candidates to choose the one that best fits their needs. Finding a relevant library replacement is a well known tedious and time-consuming task. In this paper, we propose an approach that identifies sets of similar libraries and that produces what we call library migration graphs that show how existing projects have performed migrations among them. These graphs, constructed from the observation of a large number of software projects, ease the discovery and selection of library replacements.

/pdf/mining-library-migration-graphs-2fguq49tip.pdf

Mining Library Migration Graphs

The open-source phenomenon has reached the point in which it is virtually impossible to find large applications that do not rely on it. Such grand adoption may turn into a risk if the community regulatory aspects behind open-source work (e.g., contribution guidelines or release schemas) are left implicit and their effect untracked. We advocate the explicit study and automated support of such aspects and propose Yoshi (Y ielding O pen-S ource H ealth I nformation), a tool able to map open-source communities onto community patterns, sets of known organisational and social structure types and characteristics with measurable core attributes. This mapping is beneficial since it allows, for example, (a) further investigation of community health measuring established characteristics from organisations research, (b) reuse of pattern-specific best-practices from the same literature, and (c) diagnosis of organisational anti-patterns specific to open-source, if any. We evaluate the tool in a quantitative empirical study involving 25 open-source communities from GitHub, finding that the tool offers a valuable basis to monitor key community traits behind open-source development and may form an effective combination with web-portals such as OpenHub or Bitergia. We made the proposed tool open source and publicly available.

/pdf/discovering-community-patterns-in-open-source-a-systematic-3jsdib63m4.pdf

Discovering community patterns in open-source: a systematic approach and its evaluation

Context Mining software repositories has emerged as a research direction over the past decade, achieving substantial success in both research and practice to support various software maintenance tasks Software repositories include bug repository, communication archives, source control repository, etc When using these repositories to support software maintenance, inclusion of irrelevant information in each repository can lead to decreased effectiveness or even wrong results Objective This article aims at selecting the relevant information from each of the repositories to improve effectiveness of software maintenance tasks Method For a maintenance task at hand, maintainers need to implement the maintenance request on the current system In this article, we propose an approach, MSR4SM, to extract the relevant information from each software repository based on the maintenance request and the current system That is, if the information in a software repository is relevant to either the maintenance request or the current system, this information should be included to perform the current maintenance task MSR4SM uses the topic model to extract the topics from these software repositories Then, relevant information in each software repository is extracted based on the topics Results MSR4SM is evaluated for two software maintenance tasks, feature location and change impact analysis, which are based on four subject systems, namely jEdit, ArgoUML, Rhino and KOffice The empirical results show that the effectiveness of traditional software repositories based maintenance tasks can be greatly improved by MSR4SM Conclusions There is a lot of irrelevant information in software repositories Before we use them to implement a maintenance task at hand, we need to preprocess them Then, the effectiveness of the software maintenance tasks can be improved

MSR4SM: Using topic models to effectively mining software repositories for software maintenance tasks

Background After many years of research on software repositories, the knowledge for building mature, reusable tools that perform data retrieval, storage and basic analytics is readily available. However, there is still room to improvement in the area of reusable tools implementing this knowledge. Goal To produce a reusable toolset supporting the most common tasks when retrieving, curating and visualizing data from software repositories, allowing for the easy reproduction of data sets ready for more complex analytics, and sparing the researcher or the analyst of most of the tasks that can be automated. Method Use our experience in building tools in this domain to identify a collection of scenarios where a reusable toolset would be convenient, and the main components of such a toolset. Then build those components, and refine them incrementally using the feedback from their use in both commercial, community-based, and academic environments. Results GrimoireLab, an efficient toolset composed of five main components, supporting about 30 different kinds of data sources related to software development. It has been tested in many environments, for performing different kinds of studies, and providing different kinds of services. It features a common API for accessing the retrieved data, facilities for relating items from different data sources, semi-structured storage for easing later analysis and reproduction, and basic facilities for visualization, preliminary analysis and drill-down in the data. It is also modular, making it easy to support new kinds of data sources and analysis. Conclusions We present a mature toolset, widely tested in the field, that can help to improve the situation in the area of reusable tools for mining software repositories. We show some scenarios where it has already been used. We expect it will help to reduce the effort for doing studies or providing services in this area, leading to advances in reproducibility and comparison of results.

GrimoireLab: A toolset for software development analytics.

Mining Software Repositories (MSR) is a growing area of Software Engineering (SE) research. Since their emergence in 2004, many investigations have analysed different aspects of these studies. However, there are no guidelines on how to conduct systematic MSR studies. There is a need to evaluate how MSR research is approached to provide a framework to do so systematically. To identify how MSR studies are conducted in terms of repository selection and data extraction. To uncover potential for improvement in directing systematic research and providing guidelines to do so. A systematic literature review of MSR studies was conducted following the guidelines and template proposed by Mian et al. (which refines those provided by Kitchenham and Charters). These guidelines were extended and revised to provide a framework for systematic MSR studies. MSR studies typically do not follow a systematic approach for repository selection, and many do not report selection or data extraction protocols. Furthermore, few manuscripts discuss threats to the study’s validity due to the selection or data extraction steps followed. Although MSR studies are evidence-based research, they seldom follow a systematic process. Hence, there is a need for guidelines on how to conduct systematic MSR studies. New guidelines and a template have been proposed, consolidating related studies in the MSR field and strategies for systematic literature reviews. 

A systematic process for Mining Software Repositories: Results from a systematic literature review

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