Estimating the Probability of Failure When Testing Reveals No Failures
IEEE Transactions on Software Engineering
An empirical evaluation (and specification) of the all-du-paths testing criterion
Software Engineering Journal
Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Reducing the cost of mutation testing: an empirical study
Journal of Systems and Software
On the Use of Testability Measures for Dependability Assessment
IEEE Transactions on Software Engineering
Software testing and reliability
Handbook of software reliability engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
IEEE Transactions on Software Engineering - Special issue on formal methods in software practice
Representation of propositional expert systems as partial functions
Artificial Intelligence
Reliability Testing of Rule-Based Systems
IEEE Software
Inconsistency Handling in Multiperspective Specifications
IEEE Transactions on Software Engineering
Machine Learning
Validating Requirements for Fault Tolerant Systems using Model Checking
ICRE '98 Proceedings of the 3rd International Conference on Requirements Engineering: Putting Requirements Engineering to Practice
An Empirical Investigation of Multiple Viewpoint Reasoning in Requirements Engineering
RE '99 Proceedings of the 4th IEEE International Symposium on Requirements Engineering
An Empirical Study of the Effects of Minimization on the Fault Detection Capabilities of Test Suites
ICSM '98 Proceedings of the International Conference on Software Maintenance
Mutation analysis of program test data
Mutation analysis of program test data
On mutation
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
IEEE Software
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
Just enough learning (of association rules): the TAR2 "Treatment" learner
Artificial Intelligence Review
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The behavior of nondeterminate systems can be hard to predict, since similar inputs at different times can generate different outputs. In other words, the behavior seen during testing process may not be seen at runtime. Due to the uncertainties associated with nondeterminism, the standard view is that we should avoid such nondeterminate systems, especially for systems requiring high reliability. While this is a valid guideline, at least in two application areas such nondeterminacy is unavoidable. Early life cycle requirements and AI software are becoming widely used. Yet, both are imprecise and may exhibit nondeterminate behaviour if explored rigorously by a test device. Based on a literature review and some theoretical studies, we argue that many stable properties exist within the space of all possible nondeterminate behaviors. However, we also show that seemingly trivial changes to a nondeterministic system can turn an easily testable system into an impossibly hard system to test. Finally, we stress that this analysis does not imply a correlation between stable zones of nondeterminate testability and the ultimate maintainability of nondeterminate systems. That is, while we are optimistic about testing nondeterminate systems we remain cautious about the maintenance of such systems.