Uncovering the Implicit: A Comparative Evaluation of Modeling Approaches for Environmental Assumptions

This paper presents an extended investigation into the role of environmental assumptions in the emergence and management of Requirements Technical Debt (RTD). Building on earlier work that identified environmental assumptions as a critical yet often overlooked source of RTD, we provide a comparative evaluation of seven representative modeling frameworks: KAOS, i*, Obstacle Analysis, Failure Frames, Claims, SysML, and RDAL. The analysis is structured around five dimensions: modeling focus, tracing support, validation capability, integration readiness, and assumption evolution. The results show substantial variation in support for assumption management. Among the frameworks, KAOS and Obstacle Analysis stand out for explicitly modeling assumptions and their violations. SysML, on the other hand, is particularly strong in its ability to integrate with industrial toolchains. RDAL demonstrates its greatest strength in tracing, where assumptions, requirements, and verification conditions are systematically linked, though its explicit modeling support remains more limited compared to goal-oriented approaches. i* and Claims capture assumptions more implicitly, with weaker validation and evolution capabilities, while Failure Frames focus on assumption violations but lack integration with broader MBSE workflows. The paper’s main contribution is a synthesis of trade-offs across assumption-aware modeling frameworks, highlighting both their strengths and remaining gaps. This provides actionable insights for researchers and practitioners in selecting, combining, or extending modeling approaches to better manage environmental assumptions and mitigate assumption-related technical debt.