Solving OCL Constraints for Test Data Generation in Industrial Systems with Search Techniques

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 solving OCL constraints, which is a necessary step to generate appropriate test data. Though search-based software testing 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 targeted to OCL constraints to guide test data generation and automate MBT in industrial applications. These heuristics are used to develop an efficient OCL solver exclusively based on search. In this paper, we evaluate these heuristics for search algorithms such as Genetic Algorithms, (1+1) Evolutionary Algorithm and Alternating Variable Method. We empirically evaluate our heuristics using complex artificial problems followed by empirical analyses to evaluate the feasibility of our approach on one industrial system. Though the focus is on OCL constraints, many of the principles introduced here could be adapted to other high level constraint languages based on first-order logic and set theory.