We provide an implementation of the automata learning software described in the associated ECOOP article. In particular, the artifact is a Docker image with the source code for nerode and nerode-learn, along with the scripts and benchmark inputs needed to reproduce the experiments described in the paper.
@Article{moeller_et_al:DARTS.9.2.21, author = {Moeller, Mark and Wiener, Thomas and Solko-Breslin, Alaia and Koch, Caleb and Foster, Nate and Silva, Alexandra}, title = {{Automata Learning with an Incomplete Teacher (Artifact)}}, pages = {21:1--21:3}, journal = {Dagstuhl Artifacts Series}, ISSN = {2509-8195}, year = {2023}, volume = {9}, number = {2}, editor = {Moeller, Mark and Wiener, Thomas and Solko-Breslin, Alaia and Koch, Caleb and Foster, Nate and Silva, Alexandra}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.9.2.21}, URN = {urn:nbn:de:0030-drops-182612}, doi = {10.4230/DARTS.9.2.21}, annote = {Keywords: Finite Automata, Active Learning, SMT Solvers} }
4365265d0d7390aa915d8aee84cdd1cc
(Get MD5 Sum)
The artifact has been evaluated as described in the ECOOP 2023 Call for Artifacts and the ACM Artifact Review and Badging Policy
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