Communications of the ACM
Learning regular sets from queries and counterexamples
Information and Computation
Testing by means of inductive program learning
ACM Transactions on Software Engineering and Methodology (TOSEM)
A Methodology for LISP Program Construction from Examples
Journal of the ACM (JACM)
Assessing Test Data Adequacy through Program Inference
ACM Transactions on Programming Languages and Systems (TOPLAS)
Machine Learning
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Improving test suites via operational abstraction
Proceedings of the 25th International Conference on Software Engineering
Improving dynamic software analysis by applying grammar inference principles
Journal of Software Maintenance and Evolution: Research and Practice - Special Issue on Program Comprehension through Dynamic Analysis (PCODA)
Smyle: A Tool for Synthesizing Distributed Models from Scenarios by Learning
CONCUR '08 Proceedings of the 19th international conference on Concurrency Theory
Evaluation and Comparison of Inferred Regular Grammars
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Iterative Refinement of Reverse-Engineered Models by Model-Based Testing
FM '09 Proceedings of the 2nd World Congress on Formal Methods
On the correspondence between conformance testing and regular inference
FASE'05 Proceedings of the 8th international conference, held as part of the joint European Conference on Theory and Practice of Software conference on Fundamental Approaches to Software Engineering
LearnLib: a library for automata learning and experimentation
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
Assessing test adequacy for black-box systems without specifications
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
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Inductive inference is the process of hypothesizing a model from a set of examples. It can be considered to be the inverse of program testing, which is the process of generating a finite set of tests that are intended to fully exercise a software system. This relationship has been acknowledged for almost 30 years, and has led to the emergence of several induction-based techniques that aim either to generate suitable test sets or assess the adequacy of existing test sets. Unfortunately these techniques are usually deemed to be too impractical, because they are based on exact inference, requiring a vast set of examples or tests. In practice a test set can still be adequate if the inferred model contains minor errors. This paper shows how the Probably Approximately Correct (PAC) framework, a well-established approach in the field of inductive inference, can be applied to inductive testing techniques. This facilitates a more pragmatic assessment of these techniques by allowing for a degree of error. This evaluation framework gives rise to a challenge: To identify the best combination of testing and inference techniques that produce practical and (approximately) adequate test sets.