A Search-Based OCL Constraint Solver for Model-Based Test Data Generation

Abstract

Model-based testing (MBT) aims at automated, scalable, and systematic testing solutions for complex industrial software systems. To increase chances of adoption in industrial contexts, software systems should be modeled using well-established standards such as the Unified Modeling Language (UML) and Object Constraint Language (OCL). Given that test data generation is one of the major challenges to automate MBT, this is the topic of this paper with a specific focus on test data generation from OCL constraints. Though search-based software testing (SBST) has been applied to test data generation for white-box testing (e.g., branch coverage), its application to the MBT of industrial software systems has been limited. In this paper, we propose a set of search heuristics based on OCL constraints to guide test data generation and automate MBT in industrial applications. These heuristics are used to develop an OCL solver exclusively based on search, in this particular case genetic algorithm and (1+1) EA. Empirical analyses to evaluate the feasibility of our approach are carried out on one industrial system.