Developing tools in the context of autonomous systems [22, 24], such as self-driving cars (SDCs), is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation software. We propose SensoDat, a dataset of 32,580 executed simulation-based SDC test cases generated with state-of-the-art test generators for SDCs. The dataset consists of trajectory logs and a variety of sensor data from the SDCs (e.g., rpm, wheel speed, brake thermals, transmission, etc.) represented as a time series. In total, SensoDat provides data from 81 different simulated sensors. Future research in the domain of SDCs does not necessarily depend on executing expensive test cases when using SensoDat. Furthermore, with the high amount and variety of sensor data, we think SensoDat can contribute to research, particularly for AI development, regression testing techniques for simulation-based SDC testing, flakiness in simulation, etc. Link to the dataset: https://doi.org/10.5281/zenodo.10307479

GitHub Actions (GA) enjoy increasing popularity in many software development projects as a means to automate repetitive software engineering tasks by enabling programmable event-driven workflows. Researchers typically analyze GA at the raw data level using batch tools to mine and analyze actions, jobs, and steps within GA workflows. Although this approach is widely applicable, it ignores the specific context of the GA workflow domain. Consequently, researchers do not reason directly about the domain abstractions.We present our preliminary steps in building EGAD (Explorable GitHub Action Domain Model), a moldable domain-specific tool to depict and analyze detailed GA workflow data. EGAD consists of an explorable domain model of GA workflows augmented with custom, domain-specific views, and live narratives. We illustrate EGAD in action using it to explore "sticky commits" in GitHub repositories.