Active Automata Learning in Practice - An Annotated Bibliography of the Years 2011 to 2016

  title={Active Automata Learning in Practice - An Annotated Bibliography of the Years 2011 to 2016},
  author={Falk Howar and Bernhard Steffen},
  booktitle={Machine Learning for Dynamic Software Analysis},
Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer. As systems become ever more complex and development becomes more distributed, inferred models of system behavior become an increasingly valuable asset for understanding and analyzing a system’s behavior. Five years ago (in 2011) we have surveyed the then current state of active automata learning research and applications of active automata learning in practice. We predicted four major topics to… 
Active Learning of Abstract System Models from Traces using Model Checking
A new active model-learning approach to generating abstractions of a system implementation, as finite state automata (FSAs), from execution traces, using a pluggable model learning component that can generate an FSA from a given set of traces.
Grey-Box Learning of Register Automata
This article provides new implementations of the tree oracle and equivalence oracle from RALib, which use the derived constraints from runs of Python programs, and extracts the constraints on input and output parameters from a run, and makes this grey-box information available to the learner.
Efficient learning and analysis of system behavior
Improved learning algorithm that is able to deal with huge alphabets and bandwidth reduction techniques, originally designed for sparse matrix solvers, are very capable at reducing the memory footprint of the specifications' symbolic state space.
Benchmarks for Automata Learning and Conformance Testing
A large collection of benchmarks, publicly available through the wiki, of different types of state machine models: DFAs, Moore machines, Mealy machines, interface automata and register automata will allow researchers to evaluate the performance of new algorithms and tools for active automata learning and conformance testing.
L*-Based Learning of Markov Decision Processes (Extended Version)
A novel learning algorithm is presented that learns the complete model structure including the state space by sampling execution traces of the system via testing, in contrast to existing learning algorithms which assume a predefined number of states.
Sound Black-Box Checking in the LearnLib
The LearnLib’s system-under-learning API for sound BBC is extended by means of state equivalence, that contrasts the original proposal where an upper-bound on the number of states in the system is assumed.
Query Learning Algorithm for Residual Symbolic Finite Automata
The implementation of the algorithm efficiently learns RSFAs over a huge alphabet and outperforms an existing learning algorithm for deterministic SFAs and shows that the benefit of non-determinism in efficiency is even larger in learning SFAs than non-symbolic automata.
Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions
This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning- based formal methods.
Constraint-Based Behavioral Consistency of Evolving Software Systems
The main idea is to combine software analysis approaches represented by various forms of static analysis and formal verification with runtime verification, monitoring, and automata learning in order to optimally leverage the de facto observed behaviour of the deployed systems.


Foundations of active automata learning: an algorithmic perspective
One of the stated goals of this thesis is to change this situation, by giving a rigorously formal description of an approach to active automata learning that is independent of specific data structures or algorithmic realizations.
Active Learning of Nondeterministic Systems from an ioco Perspective
Model-based testing allows the creation of test cases from a model of the system under test. Often, such models are difficult to obtain, or even not available. Automata learning helps in inferring
Learning deterministic probabilistic automata from a model checking perspective
This paper shows how to extend the basic algorithm to also learn automata models for both reactive and timed systems and establishes theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic.
Dynamic testing via automata learning
This paper presents dynamic testing, a method that exploits automata learning to systematically test (black box) systems almost without prerequisites. Based on interface descriptions and optional
The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning
The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity, thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information.
A framework for mining hybrid automata from input/output traces
A framework for analyzing multiple input/output traces by identifying steps like segmentation, clustering, generation of event traces, and automata inference is proposed, which is general enough to admit multiple techniques or future enhancements of these steps.
A Theory of History Dependent Abstractions for Learning Interface Automata
This article gets rid of some of these limitations and presents four important generalizations/improvements of the theory of history dependent abstraction operators that are conceptually superior approach for testing correctness of the hypotheses that are generated by the learner.
The Teachers' Crowd: The Impact of Distributed Oracles on Active Automata Learning
In this paper we address the major bottleneck of active automata learning, the typically huge number of required tests, by investigating the impact of using a distributed testing environment (a crowd
Inferring Automata with State-Local Alphabet Abstractions
This paper combines the automated alphabet abstraction approach, which refines the global alphabet of the system to be learned on the fly during the learning process, with the principle of state-local alphabets: rather than determining a single global alphabet, this approach infer the optimal alphabet abstraction individually for each state.
Inferring Canonical Register Automata
This paper presents an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow and drastically outperforms the classic L * algorithm, even when exploiting optimal data abstraction and symmetry reduction.