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RPNI Passive Deterministic Automata Learning

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Edi Muškardin edited this page May 12, 2022 · 5 revisions

RPNI - Passive learning of DFAs, Moore, and Mealy machines

Note: current implementation is relatively inefficient, due to absence of merging heuristics. We will consider adding them in the future. For small automata or relatively small data sets, AALpy's RPNI will perform well. Alternatively, we suggest using felxfringe.

Overview

Home

Interactive Examples

Code Examples

Core Functionalities

Learning Black-box Systems

Conformance Checking

Setting Up Learning

Helper Functionalities

Random Automata Generation

Saving, Loading, Visualizing Automata

Advanced Use

Learning-based Testing

Learning-based Fuzzing

Model-Checking With PRISM

Other

Performance Evaluation

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