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System-level testing of avionics software systems requires compliance withdifferent international safety standards such as DO-178C. An importantconsideration of the avionics industry is automated test data generationaccording to the criteria suggested by safety standards. One of the recommendedcriteria by DO-178C is the modified condition/decision coverage (MC/DC)criterion. The current model-based test data generation approaches useconstraints written in Object Constraint Language (OCL), and apply searchtechniques to generate test data. These approaches either do not support MC/DCcriterion or suffer from performance issues while generating test data forlarge-scale avionics systems. In this paper, we propose an effective way toautomate MC/DC test data generation during model-based testing. We develop astrategy that utilizes case-based reasoning (CBR) and range reductionheuristics designed to solve MC/DC-tailored OCL constraints. We performed anempirical study to compare our proposed strategy for MC/DC test data generationusing CBR, range reduction, both CBR and range reduction, with an originalsearch algorithm, and random search. We also empirically compared our strategywith existing constraint-solving approaches. The results show that both CBR andrange reduction for MC/DC test data generation outperform the baselineapproach. Moreover, the combination of both CBR and range reduction for MC/DCtest data generation is an effective approach compared to existing constraintsolvers.

Efficient Test Data Generation for MC/DC with OCL and Search