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Obstacle Analysis in Requirements Engineering: Retrospective and Emerging Challenges

Letier, Emmanuel; Van Lamsweerde, Axel; (2025) Obstacle Analysis in Requirements Engineering: Retrospective and Emerging Challenges. IEEE Transactions on Software Engineering pp. 1-7. 10.1109/TSE.2025.3534318. (In press). Green open access

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Abstract

With the growing adoption of AI-based systems, effective risk management is more important than ever. Obstacle analysis is a requirements engineering technique introduced three decades ago for designing dependable software systems despite failures, exceptions, and unforeseen behaviors in both the software and its environment. An obstacle is an undesirable situation that violates a stakeholder goal, an environment assumption, or a software requirement. Obstacles include safety hazards, security threats, user errors, and other adverse situations. Obstacle analysis provides a structured, systematic approach for identifying, analyzing, and resolving obstacles at the requirements level. In this retrospective paper, we summarize the original technique and discuss its impacts on research and practice. We also propose a research agenda to extend obstacle analysis to address emerging challenges in AI systems engineering.

Type: Article
Title: Obstacle Analysis in Requirements Engineering: Retrospective and Emerging Challenges
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TSE.2025.3534318
Publisher version: https://doi.org/10.1109/TSE.2025.3534318
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Obstacle analysis, risk analysis, fault-tolerance, exception handling, goal-oriented requirements engineering, formal specification, AI engineering
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10204032
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