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Software verification may yield spurious failures when environmentassumptions are not accounted for. Environment assumptions are the expectationsthat a system or a component makes about its operational environment and areoften specified in terms of conditions over the inputs of that system orcomponent. In this article, we propose an approach to automatically inferenvironment assumptions for Cyber-Physical Systems (CPS). Our approach improvesthe state-of-the-art in three different ways: First, we learn assumptions forcomplex CPS models involving signal and numeric variables; second, the learnedassumptions include arithmetic expressions defined over multiple variables;third, we identify the trade-off between soundness and informativeness ofenvironment assumptions and demonstrate the flexibility of our approach inprioritizing either of these criteria.  We evaluate our approach using a public domain benchmark of CPS models fromLockheed Martin and a component of a satellite control system from LuxSpace, asatellite system provider. The results show that our approach outperformsstate-of-the-art techniques on learning assumptions for CPS models, andfurther, when applied to our industrial CPS model, our approach is able tolearn assumptions that are sufficiently close to the assumptions manuallydeveloped by engineers to be of practical value.

Combining Genetic Programming and Model Checking to Generate Environment Assumptions