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
Cyber–Physical Systems (CPSs) operate under uncertainty and cannot always guarantee the satisfaction of dependability requirements. Proactive self-adaptation mitigates violations by planning corrective actions, often leveraging predictive models. These models can be inaccurate in underrepresented regions of the operational space, leading to ineffective or unsafe adaptations. We present Verify, Augment, and Improve (VAI), a framework that extends a standard MAPE–K architecture with an asynchronous self-improving loop. VAI intercepts ineffective adaptation actions and turns explanations from a descriptive aid into a mechanism for continual improvement of the self-adaptation process. Specifically, each ineffective adaptation is verified against a ground truth (e.g., a high-fidelity simulator); when a drift between surrogate and ground truth is detected, VAI explains the failure, augments the training data near the drift, and retrains the surrogate. We instantiate VAI on a human–machine teaming benchmark and two study subjects adopting alternative ground truths. Experimental results show that VAI consistently reduces the relative error of adaptation decisions and increases the success rate of meeting requirements, with average gains of \(6.89\%\) and \(10.88\%\) across the two selected subjects.
Pietro Benecchi, Luigi Cardone, Matteo Camilli et al.· SEAMS@ICSE· 0 citations
