CAES3AR: Collaborative and Efficient Scientific Software Support Architecture

Scientific software is essential for accelerating research and enabling transparent, reproducible results, but increasing adoption also increases support demands that can overwhelm small academic development teams. Since most scientists are not trained as software engineers, early-stage research software often lacks the resources and structure needed for broader use, making streamlined support workflows crucial for both users and developers. Addressing these issues is essential to ensure that researchers can focus on their core activities while streamlining processes that benefit both users and developers.

Our project CAES³AR (Collaborative and Efficient Scientific Software Support Architecture) aims to provide researchers with a more open and efficient infrastructure for software support by developing a collaborative architecture. The framework is currently being developed and evaluated using pyGIMLi, an open-source library for modeling and inversion in geophysics (www.pygimli.org), while being designed to remain transferable to a broad range of open-source projects. Thanks to its practicality and gallery of existing examples, pyGIMLi has become widely adopted in the near-surface geophysical community. At the same time, its use across diverse user environments introduces recurring support challenges, since variations in operating systems and installed dependencies can make issue reproduction and debugging time-intensive, which often reduces the capacity for methodological and software innovation.

To address these challenges efficiently, the CAES³AR framework aims to automate key aspects of user support through a generic toolchain that integrates seamlessly with existing infrastructures such as GitHub and Jupyter. It facilitates user engagement by allowing them to create GitHub or GitLab issues that include links to temporary code execution environments (e.g., JupyterLab) equipped with collaborative editing features—potentially integrated with existing JupyterHub and cloud-based infrastructures. Additionally, automated bots powered by GitHub Actions or GitLab jobs will provide real-time feedback on whether issues exist across all platforms and with the latest software versions. If a problem persists, supporters can directly modify the user's code within Jupyter without requiring any downloads or installations. Proposed changes will be presented as formatted code alterations (“diffs”) attributed to their authors in the Git issue for future reference, ensuring clarity and continuity even after the temporary JupyterHub instance is no longer available.

We recently hosted a community workshop to assess developer and user needs, identify challenges in current support practices, and gather requirements for practical adoption. This presentation summarizes key findings from those discussions and introduces early CAES³AR prototypes developed for the pyGIMLi ecosystem. As CAES³AR remains in active development, we conclude by inviting community feedback on additional features and design priorities, with the broader aim of ensuring transferability and long-term utility across multiple open-source scientific software projects.

Project website: https://caesar.pages.rwth-aachen.de/