Goal-conflict likelihood assessment based on model counting

In goal-oriented requirements engineering approaches, conflict analysis has been proposed as an abstraction for risk analysis. Intuitively, given a set of expected goals to be achieved by the system-to-be, a conflict represents a subtle situation that makes goals diverge, i.e., not be satisfiable as a whole. Conflict analysis is typically driven by the identify-assess-control cycle, aimed at identifying, assessing and resolving conflicts that may obstruct the satisfaction of the expected goals. In particular, the assessment step is concerned with evaluating how likely the identified conflicts are, and how likely and severe are their consequences.

So far, existing assessment approaches restrict their analysis to obstacles (conflicts that prevent the satisfaction of a single goal), and assume that certain probabilistic information on the domain is provided, that needs to be previously elicited from experienced users, statistical data or simulations. In this paper, we present a novel automated approach to assess how likely a conflict is, that applies to general conflicts (not only obstacles) without requiring probabilistic information on the domain. Intuitively, given the LTL formulation of the domain and of a set of goals to be achieved, we compute goal conflicts, and exploit string model counting techniques to estimate the likelihood of the occurrence of the corresponding conflicting situations and the severity in which these affect the satisfaction of the goals. This information can then be used to prioritize conflicts to be resolved, and suggest which goals to drive attention to for refinements.