Multi-objective reasoning with constrained goal models

• Published in: Requirements engineering Vol. 23; no. 2; pp. 189 - 225
• Main Authors: Nguyen, Chi Mai, Sebastiani, Roberto, Giorgini, Paolo, Mylopoulos, John
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2018; Springer Nature B.V
• Subjects: Automated reasoning; Automation; Computer Science; Constraints; Mathematical models; Multiple objective analysis; Optimization; Original Article; Reasoning programs; Software Engineering; Requirements engineering; Goal models; SAT/SMT/OMT
• ISSN: 0947-3602; 1432-010X
• DOI: 10.1007/s00766-016-0263-5

Goal models have been widely used in computer science to represent software requirements, business objectives, and design qualities. Existing goal modelling techniques, however, have shown limitations of expressiveness and/or tractability in coping with complex real-world problems. In this work, we exploit advances in automated reasoning technologies, notably satisfiability and optimization modulo theories (SMT/OMT), and we propose and formalize: (1) an extended modelling language for goals, namely the constrained goal model (CGM), which makes explicit the notion of
goal refinement
and of
domain assumption
, allows for expressing
preferences
between goals and refinements and allows for associating
numerical attributes
to goals and refinements for defining
constraints
and
optimization goals
over multiple
objective functions
, refinements, and their numerical attributes; (2) a novel set of automated reasoning functionalities over CGMs, allowing for automatically generating suitable refinements of input CGMs, under user-specified assumptions and constraints, that also maximize preferences and optimize given objective functions. We have implemented these modelling and reasoning functionalities in a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning backend. Moreover, we have conducted an experimental evaluation on large CGMs to support the claim that our proposal scales well for goal models with 1000s of elements.