A functional approach to program testing and analysis
IEEE Transactions on Software Engineering
Axiomatizing software test data adequacy
IEEE Transactions on Software Engineering
Functional program testing and analysis
Functional program testing and analysis
The Path Prefix Software Testing Strategy
IEEE Transactions on Software Engineering
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Art of Software Testing
ICSE '81 Proceedings of the 5th international conference on Software engineering
CASE tool architecture for knowledge-based regression testing
TRI-Ada '93 Proceedings of the conference on TRI-Ada '93
ADTEST: A Test Data Generation Suite for Ada Software Systems
IEEE Transactions on Software Engineering
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
A heuristic approach for test case generation
CSC '91 Proceedings of the 19th annual conference on Computer Science
Test Case Generation as an AI Planning Problem
Automated Software Engineering
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Rapid goal-oriented automated software testing using MEA-graph planning
Software Quality Control
RUBASTEM: a method for testing VHDL behavioral models
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Journal of Automated Reasoning
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Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.