- author = "Khouloud Gaaloul and Claudio Menghi and Shiva Nejati and Lionel C. Briand and Yago Isasi Parache",
- title = "Combining Genetic Programming and Model Checking to Generate Environment Assumptions",
- howpublished = "arXiv",
- year = "2021",
- month = "6 " # jan,
- keywords = "genetic algorithms, genetic programming, SBSE, Environment assumptions, Model checking, Machine learning, Decision trees, simulink, Search-based software testing, EPIcuRus, Matlab, GPLAB",
-
URL = "
https://arxiv.org/abs/2101.01933",
-
code_url = "https://github.com/SNTSVV/EPIcuRus",
- size = "20 pages",
-
abstract = "Software verification may yield spurious failures when
environment assumptions are not accounted for.
Environment assumptions are the expectations that a
system or a component makes about its operational
environment and are often specified in terms of
conditions over the inputs of that system or component.
we propose an approach to automatically infer
environment assumptions for Cyber-Physical Systems
(CPS). Our approach improves the state-of-the-art in
three different ways: First, we learn assumptions for
complex CPS models involving signal and numeric
variables; second, the learned assumptions include
arithmetic expressions defined over multiple variables;
third, we identify the trade-off between soundness and
informativeness of environment assumptions and
demonstrate the flexibility of our approach in
prioritising either of these criteria.
We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.",
