Efficient Test Data Generation for MC/DC with OCL and Search (2401.03469v3)

Abstract: System-level testing of avionics software systems requires compliance with different international safety standards such as DO-178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO-178C is the modified condition/decision coverage (MC/DC) criterion. The current model-based test data generation approaches use constraints written in Object Constraint Language (OCL), and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large-scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model-based testing. We develop a strategy that utilizes case-based reasoning (CBR) and range reduction heuristics designed to solve MC/DC-tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint-solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers.