@Article{SelvarajFarooquiEtAl2022,
AUTHOR = {Selvaraj, Yuvaraj and Farooqui, Ashfaq and Panahandeh, Ghazaleh and Ahrendt, Wolfgang and Fabian, Martin},
TITLE = {Automatically Learning Formal Models from Autonomous Driving Software},
JOURNAL = {Electronics},
VOLUME = {11},
YEAR = {2022},
NUMBER = {4},
ARTICLE-NUMBER = {643},
URL = {https://www.mdpi.com/2079-9292/11/4/643},
ISSN = {2079-9292},
ABSTRACT = {The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed.},
DOI = {10.3390/electronics11040643}
}