As automation becomes increasingly central to software engineering, the need for systems that can explain their behavior is urgent. The 1st International Workshop on Explainable Automated Software Engineering (Ex-ASE 2025) provided a venue to explore how explanations can be integrated into automated processes, from requirements to testing and deployment, to support transparency, accountability, and trust. This report summarizes the workshop's inaugural edition, featuring a keynote on Large Language Model (LLM) reasoning and technical papers addressing developer comprehension, AI testing, and high-stakes domains.
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