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.

翻译：航空电子软件系统的系统级测试需遵循DO-178C等国际安全标准。航空电子行业的一个重要考量是依据安全标准建议的准则实现自动化测试数据生成。DO-178C推荐的准则之一是修正条件/判定覆盖（MC/DC）准则。当前基于模型的测试数据生成方法采用对象约束语言（OCL）编写的约束条件，并应用搜索技术生成测试数据。这些方法要么不支持MC/DC准则，要么在为大规模航空电子系统生成测试数据时存在性能问题。本文提出一种在基于模型的测试中实现MC/DC测试数据自动生成的有效方法。我们开发了一种综合利用案例推理（CBR）与针对MC/DC定制化OCL约束设计的范围缩减启发式策略。我们通过实证研究，将所提出的基于CBR、范围缩减以及两者结合的MC/DC测试数据生成策略，与原始搜索算法及随机搜索进行对比。同时，我们还与现有约束求解方法进行了实证比较。结果表明，针对MC/DC测试数据生成的CBR与范围缩减策略均优于基线方法。此外，结合CBR与范围缩减的MC/DC测试数据生成方法相较于现有约束求解器是一种更有效的解决方案。