International audience

Git social platforms (such as Gitlab, Github, or Bitbucket) provide insight into a team’s workflow. Mining Software Repositories (MSR) provides methods and tools to extract data from these platforms. However, most tools lack connectivity and extensibility across multiple platforms. Moreover, they rarely connect to other project management platforms such as Jira. In this paper, we introduce GitProjectHealth (GPH), a framework to extract data from any Git repositories and social platforms. GPH is implemented inside a model-driven engineering framework in Pharo smalltalk, facilitating its extension to other social platforms. We demonstrate GPH features over 3 open-source organizations: Eclipse, MooseTechnology and Microsoft; as well as Berger-Levrault, a closed-source company. We extracted their activity to build distributions of commits by user and to determine which types of ticket were associated with each merge request.

Since their introduction, mobile application development has become complex and now rivals web app development. The primary factor contributing to this complexity is their monolithic architecture: a mobile application is still deployed as a single unit. In contrast, web applications have adopted modular architectures such as Microservice or Micro FrontEnd (MFE). This last approach divides development into teams, each responsible for an MFE corresponding to a specific software feature. Our study examines the adoption of the MFE architecture on native mobile platforms. This paper discusses: i) how MFE can be adapted to current native mobile platforms; ii) the limitations of adapting MFE architecture on mobile; and iii) the challenges MFE introduces to mobile application development. Despite challenges in composing mobile MFEs post-deployment, our research highlighted maintaining a static development-side composite is feasible, albeit with a loss of flexibility. This allows team separation and independent feature development, though technological agnosticism isn't achieved.

Formal Concept Analysis (FCA) has been introduced for almost a decade as a suitable method for Feature Location (FL) on a collection of product variants. Even though FCA-based FL techniques allow to locate the core of a feature implementation, they do not propose a solution to trace feature interactions to their implementation. Thus, the extracted traceability links (traces) are too inaccurate, and, in the context of SPL extraction, cannot be used to generate complete products.