arXiv subjects

Bharat Garhewal

Publications and source records attributed to Bharat Garhewal.

3 recordsLinked to original sources

arXiv2025

Active Learning of Mealy Machines with Timers

We present the first algorithm for query learning Mealy machines with timers in a black-box context. Our algorithm is an extension of the L# algorithm of Vaandrager et al. to a timed setting. We rely on symbolic queries which empower us to reason on untimed executions while learning. Similarly to the algorithm for learning timed automata of Waga, these symbolic queries can be realized using finitely many concrete queries. Experiments with a prototype implementation show that our algorithm is able to efficiently learn realistic benchmarks.

cs.FL↗

arXiv2022

A New Approach for Active Automata Learning Based on Apartness

We present $L^{\#}$, a new and simple approach to active automata learning. Instead of focusing on equivalence of observations, like the $L^{\ast}$ algorithm and its descendants, $L^{\#}$ takes a different perspective: it tries to establish apartness, a constructive form of inequality. $L^{\#}$ does not require auxiliary notions such as observation tables or discrimination trees, but operates directly on tree-shaped automata. $L^{\#}$ has the same asymptotic query and symbol complexities as the best existing learning algorithms, but we show that adaptive distinguishing sequences can be naturally integrated to boost the performance of $L^{\#}$ in practice. Experiments with a prototype implementation, written in Rust, suggest that $L^{\#}$ is competitive with existing algorithms.

cs.FL↗

arXiv2020

Grey-Box Learning of Register Automata

Model learning (a.k.a. active automata learning) is a highly effective technique for obtaining black-box finite state models of software components. Thus far, generalisation to infinite state systems with inputs/outputs that carry data parameters has been challenging. Existing model learning tools for infinite state systems face scalability problems and can only be applied to restricted classes of systems (register automata with equality/inequality). In this article, we show how we can boost the performance of model learning techniques by extracting the constraints on input and output parameters from a run, and making this grey-box information available to the learner. More specifically, we provide new implementations of the tree oracle and equivalence oracle from RALib, which use the derived constraints. We extract the constraints from runs of Python programs using an existing tainting library for Python, and compare our grey-box version of RALib with the existing black-box version on several benchmarks, including some data structures from Python's standard library. Our proof-of-principle implementation results in almost two orders of magnitude improvement in terms of numbers of inputs sent to the software system. Our approach, which can be generalised to richer model classes, also enables RALib to learn models that are out of reach of black-box techniques, such as combination locks.

cs.FL↗