AI for Software Engineering (AI4SE) Workshop

This workshop is organized in the context of the 49th German Conference on Artificial Intelligence on August 11-14, 2026, in Bremen, Germany.

Motivation

Artificial Intelligence (AI) is fundamentally transforming Software Engineering /(SE) practices, tools, and methodologies. From code generation and bug detection to requirements analysis and system maintenance, AI techniques are opening new possibilities while raising important questions about reliability, interpretability, and best practices. As both the AI and SE communities continue to evolve, there is a critical need for interdisciplinary dialogue that bridges these fields.

Workshop Goals

This workshop aims to create a collaborative space where researchers from AI and SE can come together to share their work, exchange ideas, and collectively shape the future of Software Engineering in the age of Artificial Intelligence. We welcome diverse perspectives on how AI can enhance, augment, or transform SE activities, as well as critical discussions about challenges, limitations, and ethical considerations.

Call for Papers

Topics of Interest

The workshop intends to keep the scope of application use cases as broad as possible. We want this workshop to be an open stage for AI and SE researchers to discuss AI4SE approaches that best fit given use cases. The types of work expected include (but are not limited to) proof of concept, benchmarks, empirical studies, lessons learned reports, literature reviews, position papers, and tool demonstrations.

Topics include the application of AI methods, such as machine learning, deep learning, large language models, reinforcement learning, evolutionary algorithms, knowledge representation, and reasoning techniques, to:

• Software development lifecycle activities: requirements analysis, design, development, testing, formal methods, verification and validation, maintenance, and evolution
• Fault management: fault prevention, fault removal (fault localization, debugging, root cause analysis), fault tolerance, and fault prediction
• Software Quality Assurance: assessment and improvement of software quality attributes, including security, privacy, safety, maintainability, resilience, robustness, usability, transparency, explainability, accountability, and fairness
• Empirical software engineering: mining software repositories, developer productivity, software analytics
• Domain-specific software engineering: AI systems engineering, Internet of Things, Cloud computing, Semantic Web/Web 3.0, Virtualization, Blockchain, network softwarization, 5G/6G, edge-to-cloud computing
• AI-assisted software engineering tools: code generation, automated testing, intelligent IDEs, code review assistants
• Human-AI collaboration in software engineering: developer-AI interaction, explainability, trust, and adoption
• Ethical, regulatory, and societal aspects: responsible AI in SE, compliance, bias and fairness, sustainability

Manuscript Information

Submitted papers should present original, unpublished work relevant to one of the topics above. AI4SE 2026 will accept:

• Full papers (max. 8 pages) describing original, complete, and validated research
• Position/Short papers (max. 4 pages) that describe forward-looking, visionary ideas and/or in-progress works with emerging results, thought-provoking reflections, or that set potential new directions for the community
• Tool and artifacts papers (max. 4 pages) for researchers who want to present tools, extensions of tools, or artifacts (e.g., datasets for benchmarks) relevant to the workshop

Submissions must be in English and in PDF format. At the time of submission, all papers must conform to the KI 2026 format and submission guidelines see here.

Important Dates:

• Workshop papers submission: 2026/05/31
• Workshop papers notification: 2026/06/15
• Workshop papers camera-ready (hard): 2026/08/01

Link to the submission system:

openreview

Workshop Program (TBD)

Program Committee (Alphabetical order, name)

• Viola Campos (RheinMain University of Applied Sciences, Germany)
• Jens Heidrich (Mainz University of Applied Sciences, Germany)
• Martin Kowalczyk (Mainz University of Applied Sciences, Germany)
• Andreas Schmietendorf (Berlin School of Economics and Law, Germany)
• Adrian Ulges (RheinMain University of Applied Sciences, Germany)
• Stefan Wagner (Technischen Universität München, Germany)

Organizers and contacts

Dr. Julien Siebert is a Senior Expert in Artificial Intelligence at the Fraunhofer Institute for Experimental Software Engineering (IESE), Kaiserslautern, Germany. He is guest editor of the special issue on Causal Modeling and Inference in Software Engineering (Information and Software Technology). His research interests include software engineering methods for artificial intelligence and complex systems.

Dr. Adam Trendowicz is a Senior Data Science Expert at the Fraunhofer Institute for Experimental Software Engineering (IESE), Kaiserslautern, 67663, Germany. His research interests include data quality assessment and data preparation in the context of machine learning and data-driven business innovation. Trendowicz received his Ph.D. in computer science from the University of Kaiserslautern.