Improved heuristics for solving OCL constraints using search algorithms

Abstract

The Object Constraint Language (OCL) is a standard language for specifying constraints on Unified Modeling Language (UML) models. The specified constraints can be used for various purposes including verification, and model-based testing (e.g., test data generation). Efficiently solving OCL constraints is one of the key requirements for the practical use of OCL. In this paper, we propose an improvement in existing heuristics to solve OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA). We also used Random Search (RS) as a comparison baseline. The first empirical study was conducted using carefully designed artificial problems (constraints) to assess each individual heuristics. The second empirical study is based on an industrial case study provided by Cisco about model-based testing of Video Conferencing Systems. The results of both empirical evaluations reveal that the effectiveness of the search algorithms, measured in terms of time to solve the OCL constraints to generate data, is significantly improved when using the novel heuristics presented in this paper. In particular, our experiments show that (1+1) EA with the novel heuristics has the highest success rate among all the analyzed algorithms, as it requires the least number of iterations to solve constraints.