Improving the performance of OCL constraint solving with novel heuristics for logical operations: a search-based approach

Abstract

A common practice to specify constraints on the Unified Modeling Language (UML) models is using the Object Constraint Language (OCL). Such constraints serve various purposes, ranging from simply providing precise meaning to the models to supporting complex verification and validation activities. In many applications, these constraints have to be solved to obtain values satisfying the constraints, for example, in the case of model-based testing (MBT) to generate test data for the purpose of generating executable test cases. In our previous work, we proposed novel heuristics for various OCL constructs to efficiently solve them using search algorithms. These heuristics are enhanced in this paper to further improve the performance of OCL constraint solving. We performed an empirical evaluation comprising of three case studies using three search algorithms: Alternating Variable Method (AVM), (1ź+ź1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA) and in addition Random Search (RS) was used as a comparison baseline. In the first case study, we evaluated each heuristics using carefully designed artificial problems. In the second case study, we evaluated the heuristics on various constraints of Cisco's Video Conferencing Systems defined to support MBT. Finally, the third case study is about EU-Rent Car Rental specification and is obtained from the literature. The results of the empirical evaluation showed that (1ź+ź1) EA and AVM with the improved heuristics significantly outperform the rest of the algorithms.