Print Email Facebook Twitter Inferring DFAs from Log Traces Using Community Detection Title Inferring DFAs from Log Traces Using Community Detection Author Brandirali, Tommaso (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology) Contributor Olsthoorn, Mitchell (mentor) Panichella, A. (mentor) Pawełczak, Przemysław (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-02 Abstract Large software systems today require increasingly complex models of their execution to aid the analysis of their behavior. Such execution models are impractical to compile by hand, and current approaches to their automated generation are either not generalizable or not scalable enough. This paper addresses this problem with a new approach based on the interpretation of log traces. We analyze the effectiveness of using community detection algorithms for generating system execution models from structured datasets of log samples. This approach first models sets of log traces as tree-shaped automata, and then uses graph clustering algorithms to reduce such tree representations down to more concise models. This research focuses on analysing the quality of the generated models in terms of conciseness, accuracy, recall and scalability. Testing was performed on data samples from the XRP network, a blockchain-based payment system. During implementation of a proof of concept, multiple challenges arose which limited the ability of our study to fully evaluate the approach's effectiveness. The partial results obtained show poor performance, both in terms of runtime and in accuracy of generated models. Due to the limitations of the evaluation performed, the results are to be considered exploratory and require further testing. Subject log inferencecommunity detectiondeterministic finite automataclustering To reference this document use: http://resolver.tudelft.nl/uuid:76ac48f1-7ef5-46d3-b2f3-12c556515242 Bibliographical note https://doi.org/10.5281/zenodo.5035298 Proof of concept implementation release v1.0.0 https://doi.org/10.5281/zenodo.5035325 Benchmarking dataset Part of collection Student theses Document type bachelor thesis Rights © 2021 Tommaso Brandirali Files PDF Research_Project_Final_Paper_1.pdf 542.49 KB Close viewer /islandora/object/uuid:76ac48f1-7ef5-46d3-b2f3-12c556515242/datastream/OBJ/view