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.

本文提出了一种有效的自动化修改条件 / 决策覆盖测试数据生成方法，利用基于案例推理和范围缩减启发式技术解决了修改条件 / 决策覆盖定制的对象约束语言约束问题，实验证明该方法优于现有的约束求解方法。