Learning regular sets from queries and counterexamples
Information and Computation
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Information Retrieval
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
A Comparative Study of Two Algorithms for Automata Identification
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Generating Annotated Behavior Models from End-User Scenarios
IEEE Transactions on Software Engineering
Inferring state-based behavior models
Proceedings of the 2006 international workshop on Dynamic systems analysis
QUARK: Empirical Assessment of Automaton-based Specification Miners
WCRE '06 Proceedings of the 13th Working Conference on Reverse Engineering
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
SMArTIC: towards building an accurate, robust and scalable specification miner
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Reverse Engineering State Machines by Interactive Grammar Inference
WCRE '07 Proceedings of the 14th Working Conference on Reverse Engineering
Automated discovery of state transitions and their functions in source code
Software Testing, Verification & Reliability - TAIC PART 2006 Special issue - Testing: Academic & Industrial Conference - Practice And Research Techniques
THE QSM ALGORITHM AND ITS APPLICATION TO SOFTWARE BEHAVIOR MODEL INDUCTION
Applied Artificial Intelligence
Integration testing of distributed components based on learning parameterized i/o models
FORTE'06 Proceedings of the 26th IFIP WG 6.1 international conference on Formal Techniques for Networked and Distributed Systems
Inferring Finite-State Models with Temporal Constraints
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Iterative Refinement of Reverse-Engineered Models by Model-Based Testing
FM '09 Proceedings of the 2nd World Congress on Formal Methods
The practical assessment of test sets with inductive inference techniques
TAIC PART'10 Proceedings of the 5th international academic and industrial conference on Testing - practice and research techniques
Incrementally discovering testable specifications from program executions
FMCO'09 Proceedings of the 8th international conference on Formal methods for components and objects
Search based hierarchy generation for reverse engineered state machines
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Automated Comparison of State-Based Software Models in Terms of Their Language and Structure
ACM Transactions on Software Engineering and Methodology (TOSEM)
Empirical Software Engineering
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Grammar inference is a family of machine learning techniques that aim to infer grammars from a sample of sentences in some (unknown) language. Dynamic analysis is a family of techniques in the domain of software engineering that attempts to infer rules that govern the behaviour of software systems from a sample of executions. Despite their disparate domains, both fields have broadly similar aims; they try to infer rules that govern the behaviour of some unknown system from a sample of observations. Deriving general rules about program behaviour from dynamic analysis is difficult because it is virtually impossible to identify and supply a complete sample of necessary program executions. The problems that arise with incomplete input samples have been extensively investigated in the grammar inference community. This has resulted in a number of advances that have produced increasingly sophisticated solutions that are more successful at accurately inferring grammars from (potentially) sparse information about the underlying system. This paper investigates the similarities and shows how many of these advances can be applied with similar effect to dynamic analysis problems by a series of small experiments on random state machines. Copyright © 2008 John Wiley & Sons, Ltd.