Description

This second edition of the CauSE workshop is planed to be organized in the context of the ACM International Conference on the Foundations of Software Engineering (FSE2026). Sun 5 - Thu 9 July 2026 Montreal, Canada. https://conf.researchr.org/home/fse-2026

Motivation

Despite their potential, causal methods have not yet been leveraged by the software engineering community. While preliminary studies demonstrated their benefit in specific areas, their broad and systematic exploitation for software engineering is still far from coming. The objective of this workshop is to provide the first platform for participants to share their research, experiences, and insights on causal inference methods and their applications in software engineering. The broader aim is to foster networking and open new collaboration opportunities, encouraging the development of a new strong community.

This workshop aims to bring together researchers, practitioners, and students to explore the growing field of causal inference and, more broadly, causal AI (including causal discovery, mediation analysis, counterfactual analysis, root-cause and causal attribution analysis) in software engineering. Causal inference methods allow the identification and estimation of causal effects from observational data, distinguishing between spurious correlations and causal effects. Despite a growing interest in the topic, the application of such methods in software engineering is not widespread, and there is a need to foster a community to exchange and promote scientific activities on the topic.

Workshop goals

The workshop will provide a platform for participants to share their research, experiences, and insights on causal inference methods and their applications in software engineering. It will also facilitate networking and collaboration opportunities, encouraging the development of a strong community.

Call for papers

Topics of interest:

The workshop intends to keep the scope of the application use cases as broad as possible. We don’t want to restrict the type of causal methods applied and want this workshop to be an open stage for SE researchers to discuss which causal approach fits best a given use case. The types of work expected include (but are not limited to) proof of concept, benchmarks, empirical studies, lessons learned reports, literature reviews, etc.

Topics include the application of causal reasoning methods, such as causal discovery, causal inference, and the causal treatment of machine learning (Causal Machine Learning, Causal Reinforcement Learning), to:

• Software engineering activities along the whole life cycle, from requirements analysis to design, development, testing and formal methods, verification and validation, maintenance and evolution
• Fault prevention, fault removal (fault localization, debugging, root cause analysis), fault tolerance, fault prediction
• Software Quality Assurance, assessment, and improvement of software quality attributes, e.g., security, privacy, safety, maintainability, resilience, robustness, usability, transparency, explainability, accountability, and fairness among others
• Empirical software engineering
• Software engineering within specific technological spaces (e.g., AI systems, Internet of Things, Cloud, Semantic Web/Web 3.0, Virtualization, Blockchain, networks softwarization, 5G/6G, edge-to-cloud computing)
• Normative/regulatory/ethical spaces about software engineering

Manuscript information:

Submitted papers should present original, unpublished work, relevant to one of the topics above. CauSE 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 FSE 2026 format and submission guidelines. The workshop will employ a double-anonymous review process. All submissions will be refereed by three members of the program committee. Accepted submissions will be published in FSE 2026 companion proceedings.

Important Dates:

To be announced

Link to the submission system:

https://cause2026.hotcrp.com/

Workshop Program

To be announced

Program Committee

To be announced

Organizers and contacts

Dr. Julien Siebert is a Senior Expert in Artificial Intelligence at the Fraunhofer Institute for Experimental Software Engineering, 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.

Prof. Roberto Pietrantuono is an Associate Professor at the Federico II University of Naples, working in the Dependable Systems and Software Engineering Research Team (DESSERT). He is associate editor of Transactions on Services Computing and Software Quality Journal. He is co-founder of Critiware s.r.l., a company working on critical systems engineering. He currently coordinates an EU MSCA Project (uDEVOPS) on SQA for microservice systems. His research interests include software engineering, software reliability, software testing and AI systems engineering.

Dr. Neil Walkinshaw is a Senior Lecturer at the University of Sheffield. His research focuses on software quality assurance, particularly “black-box” components, and he specializes in applying Machine Learning and data analysis algorithms to testing, reverse-engineering, and safety-case assessment. He received a grant from CITCoM (2021-2024) for the project “Causal Inference for Testing of Computational Models.”

Luca Giamattei is researcher at the Federico II University of Naples, working in the Dependable Systems and Software Engineering Research Team (DESSERT). His research interests encompass the use of causal reasoning in software testing.