Executing Model-Based Tests on Platform-Specific Implementations (T)

Dongjiang You, Sanjai Rayadurgam, M. Heimdahl, John Komp, Baekgyu Kim, O. Sokolsky
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Abstract

Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives. Previously proposed approaches address this by being reactively permissive: passing criteria are relaxed to reduce false positives, but may increase false negatives, which is particularly bothersome for safety-critical systems. To address this concern, we propose an automated approach that is proactively adaptive: test stimuli and system responses are suitably modified taking into account platform-specific aspects so that the modified test when executed on the platform-specific implementation exercises the intended scenario captured in the original model-based test. We show that the new framework eliminates false negatives while keeping the number of false positives low for a variety of platform-specific configurations.
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在平台特定实现(T)上执行基于模型的测试
嵌入式实时系统的基于模型的测试具有挑战性,因为平台特定的细节通常被抽象出来,以使模型适合各种分析。测试实现以暴露不符合这样的模型需要协调这些抽象产生的差异。由于有状态行为,模型和系统行为的天真比较经常失败,导致大量误报。先前提出的方法通过反应性宽松来解决这个问题:通过标准放宽以减少误报,但可能增加误报,这对于安全关键系统来说尤其麻烦。为了解决这个问题,我们提出了一种主动自适应的自动化方法:考虑到特定于平台的方面,适当地修改测试刺激和系统响应,以便在特定于平台的实现上执行修改后的测试时,可以练习在原始基于模型的测试中捕获的预期场景。我们展示了新框架消除了误报,同时保持了各种平台特定配置的低误报数量。
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