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Determining an optimal time interval for testing and debugging software
by
Singpurwalla, N.D.
in
Applied sciences
/ Bayesian analysis
/ Bayesian methods
/ Certification
/ Computer bugs
/ Computer science
/ Computer science; control theory; systems
/ Cost function
/ Debugging
/ Decision making
/ Decision theory
/ Design of experiments
/ Exact sciences and technology
/ Expected utility
/ Mathematical models
/ Reliability
/ Software
/ Software debugging
/ Software engineering
/ Software reliability
/ Software testing
/ Testing
/ Uncertainty
/ Utility theory
1991
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Determining an optimal time interval for testing and debugging software
by
Singpurwalla, N.D.
in
Applied sciences
/ Bayesian analysis
/ Bayesian methods
/ Certification
/ Computer bugs
/ Computer science
/ Computer science; control theory; systems
/ Cost function
/ Debugging
/ Decision making
/ Decision theory
/ Design of experiments
/ Exact sciences and technology
/ Expected utility
/ Mathematical models
/ Reliability
/ Software
/ Software debugging
/ Software engineering
/ Software reliability
/ Software testing
/ Testing
/ Uncertainty
/ Utility theory
1991
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Do you wish to request the book?
Determining an optimal time interval for testing and debugging software
by
Singpurwalla, N.D.
in
Applied sciences
/ Bayesian analysis
/ Bayesian methods
/ Certification
/ Computer bugs
/ Computer science
/ Computer science; control theory; systems
/ Cost function
/ Debugging
/ Decision making
/ Decision theory
/ Design of experiments
/ Exact sciences and technology
/ Expected utility
/ Mathematical models
/ Reliability
/ Software
/ Software debugging
/ Software engineering
/ Software reliability
/ Software testing
/ Testing
/ Uncertainty
/ Utility theory
1991
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Determining an optimal time interval for testing and debugging software
Journal Article
Determining an optimal time interval for testing and debugging software
1991
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Overview
A decision-theoretic procedure for determining an optimal time interval for testing software prior to its release is proposed. The approach is based on the principles of decision-making under uncertainty and involves a maximization of expected utility. Two plausible forms for the utility function, one based on costs and the other involving the realized reliability of the software, are described. Using previous results on probabilistic models for software failure, the ensuing optimization problem (which can be addressed using numerical techniques) is outlined for the case of single-state testing. The sensitivity of the results to the various input parameters is discussed, and some directions for future research are outlined.< >
Publisher
IEEE,Institute of Electrical and Electronics Engineers,IEEE Computer Society
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