Communications of the ACM
Machine learning of inductive bias
Machine learning of inductive bias
Specification directed module testing
IEEE Transactions on Software Engineering
A Recursion Theoretic Approach to Program Testing
IEEE Transactions on Software Engineering
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Completely validated software: test adequacy and program mutation (panel session)
ICSE '89 Proceedings of the 11th international conference on Software engineering
The coupling effect: fact or fiction
TAV3 Proceedings of the ACM SIGSOFT '89 third symposium on Software testing, analysis, and verification
Mockingbird: a logical methodology for testing
Journal of Logic Programming - Logic programming applications
Automated Test Case Generation for Programs Specified by Relational Algebra Queries
IEEE Transactions on Software Engineering
Generation of test cases for simple Prolog programs
Acta Cybernetica
Editorial: Advice to Machine Learning Authors
Machine Learning
A Fortran language system for mutation-based software testing
Software—Practice & Experience
Constraint-Based Automatic Test Data Generation
IEEE Transactions on Software Engineering
Automated Module Testing in Prolog
IEEE Transactions on Software Engineering
Using interactive concept learning for knowledge-base validation and verification
Validation, verification and test of knowledge-based systems
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)
Test-Case Generation from Prolog-Based Specifications
IEEE Software
IEEE Transactions on Knowledge and Data Engineering
The difficulties of learning logic programs with cut
Journal of Artificial Intelligence Research
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Given a program P and a set of alternative programs P, we generate a sequence of test cases that are adequate, in the sense that they distinguish the given program from all alternatives. The method is related to fault-based approaches to program testing, but programs in P need not be simple mutations of P. The technique for generating an adequate test set is based on the inductive learning of programs from finite sets of input-output examples: given a partial test set, we generate inductively a program P'E P which is consistent with P on those input values; then we look for an input value that distinguishes P from P', and repeat the process until no program except P can be induced from the generated examples. We show that the so obtained test set is adequate w.r.t. the alternatives belonging to P. The method is made possible by a practical program induction procedure, which has evolved from recent research in Machine Learning and Inductive Logic Programming.