Search‐Based MC/DC Test Data Generation With OCL Constraints

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. 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.