Proceedings of the 14th international conference on Model driven engineering languages and systems
Constraint-based model refactoring
Proceedings of the 14th international conference on Model driven engineering languages and systems
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Combining UML sequence and state machine diagrams for data-flow based integration testing
ECMFA'12 Proceedings of the 8th European conference on Modelling Foundations and Applications
Lightweight string reasoning for OCL
ECMFA'12 Proceedings of the 8th European conference on Modelling Foundations and Applications
Software and Systems Modeling (SoSyM)
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Automatic generation of test models and properties from UML models with OCL constraints
Proceedings of the 12th Workshop on OCL and Textual Modelling
Heuristic search-based approach for automated test data generation: a survey
International Journal of Bio-Inspired Computation
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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.