The category-partition method for specifying and generating fuctional tests
Communications of the ACM
Partition Testing Does Not Inspire Confidence (Program Testing)
IEEE Transactions on Software Engineering
Analyzing Partition Testing Strategies
IEEE Transactions on Software Engineering
Estimating the Probability of Failure When Testing Reveals No Failures
IEEE Transactions on Software Engineering
On the Relationship Between Partition and Random Testing
IEEE Transactions on Software Engineering
On the Expected Number of Failures Detected by Subdomain Testing and Random Testing
IEEE Transactions on Software Engineering
On the Criteria of Allocating Test Cases under Uncertainty
APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
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Previous studies have shown that partition testing strategies can be very effective in detecting faults, but they can also be less effective than random testing under unfavorable circumstances. When test cases are allocated in proportion to the size of sub-domains, partition testing strategies are provably better than random testing, in the sense of having a higher or equal probability of detecting at least one failure (the P-measure). Recently, the Generalized Proportional Sampling (GPS) strategy, which is always satisfiable, has been proposed to relax the proportionality condition. This paper studies the use of approximation methods to generate test distributions satisfying the GPS strategy, and evaluates this proposal empirically. Our results are very encouraging, showing that on average about 98.72% to almost 100% of the test distributions obtained in this way are better than random testing in terms of the P-measure.