This paper presents an automated failure analysis approach based on data mining. It aims to ease and accelerate the debugging work in formal verification based on model checking if a safety property is not satisfied. Inspired by the Kullback-Leibler Divergence theory and the TF-IDF (Term Frequency - Inverse Document Frequency) measure, we propose a suspiciousness factor to rank potentially faulty transitions on the error traces in time Petri net models. This approach is illustrated using a best case execution time property case study, and then further assessed for its efficiency and effectiveness on an automated deadlock property test bed.
CITATION STYLE
Ge, N., Pantel, M., & Crégut, X. (2014). Automated failure analysis in model checking based on data mining. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8748, 13–28. https://doi.org/10.1007/978-3-319-11587-0_4
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