Despite substantial progress in managing uncertainty in software-intensive systems, existing methods often treat uncertainty sources independently and provide limited support for understanding their combined effects. When multiple uncertainties propagate through system elements and converge at shared variables, models, or decision points, they may interact in ways that alter system behavior, compromise requirements, or invalidate assurance arguments. This challenge, referred to as the uncertainty interaction problem, remains insufficiently understood. This roadmap paper reports the outcomes of the NII Shonan Seminar No. 232 on Uncertainty Interaction in Software-Intensive Systems (UNISON), held in March 2026. It develops a shared conceptual vocabulary for distinguishing uncertainty sources, propagation, confluence, interaction, and relevance; proposes an abstract workflow for identifying, filtering, and assessing relevant uncertainty interactions; and introduces a lifecycle-oriented framework for characterizing and selecting mitigation strategies. Building on these foundations, the paper organizes open challenges into a staged research roadmap spanning conceptual consolidation, reusable methods, engineering integration, validation, tooling, and community adoption. The roadmap provides a common reference point for researchers and practitioners working across software engineering, self-adaptive systems, control, artificial intelligence, formal methods, and assurance.
Javier Cámara, R. Mirandola, Kenji Tei et al.· ACM Transactions on Autonomo...· 0 citations
To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired. Among AI components, Transformer models are increasingly integrated into software systems, which makes their misbehaviors critical. Although prior work in the software engineering community has proposed deep neural network (DNN) repair methods, most overlook Transformer-specific structures. We propose RepTran, a search-based repair method for Transformer models. It targets their feed-forward networks (FFNs), which play a central role in the architecture. RepTran identifies suspicious weights by combining two types of scores: a variance-based neuron score and an existing bidirectional score. It then iteratively optimizes these weights using differential evolution. Our evaluation includes 18 fault benchmarks constructed from CIFAR-100 and Tiny-ImageNet. We compare RepTran against three baselines: random weight selection, Arachne (a state-of-the-art DNN repair method), and ArachneW, which enables Arachne to control the number of selected weights. RepTran achieved an average repair rate of 74.7%, statistically outperforming random selection and Arachne across all benchmarks. Effect size analysis revealed that RepTran achieved higher repair rates than ArachneW regardless of the number of selected weights. These results suggest that RepTran is effective for enhancing the reliability of AI-enabled software.
Yuta Ishimoto, Paolo Arcaini, Fuyuki Ishikawa et al.· arXiv.org· 0 citations
This work creates a novel open-source symbolic traffic model EvoDrive designed specifically for EC research, which outputs LLM-readable snapshots and shows that LLMs + EvoDrive with CLEAR can reduce error by more than 20% compared to without CLEAR, with statistically significant results.
Peter J. Bentley, S. Lim, Fuyuki Ishikawa et al.· Annual Conference on Genetic...· 0 citations
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