Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Some observations on partition testing
TAV3 Proceedings of the ACM SIGSOFT '89 third symposium on Software testing, analysis, and verification
Partition Testing Does Not Inspire Confidence (Program Testing)
IEEE Transactions on Software Engineering
Analyzing Partition Testing Strategies
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
Static Assignment of Stochastic Tasks Using Majorization
IEEE Transactions on Computers
Evaluating Testing Methods by Delivered Reliability
IEEE Transactions on Software Engineering
Partition Testing vs. Random Testing: The Influence of Uncertainty
IEEE Transactions on Software Engineering
A Formal Analysis of the Fault-Detecting Ability of Testing Methods
IEEE Transactions on Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Toward More Effective Testing for High-Assurance Systems
HASE '97 Proceedings of the 2nd High-Assurance Systems Engineering Workshop
On the analytical comparison of testing techniques
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
When only random testing will do
Proceedings of the 1st international workshop on Random testing
Formal analysis of the effectiveness and predictability of random testing
Proceedings of the 19th international symposium on Software testing and analysis
Extended program invariants: applications in testing and fault localization
Proceedings of the 2012 Workshop on Dynamic Analysis
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The comparison of partition and random sampling methods for software testing has received considerable attention in the literature. A standard criterion for comparisons between random and partition testing based on their expected efficacy in program debugging is the probability of detecting at least one failure causing input in the program's domain. We investigate the relative effectiveness of partition testing versus random testing through the powerful mathematical technique of majorization, which was introduced by Hardy et al. The tools of majorization and the concepts of Schur (convex and concave) functions enable us to derive general conditions under which partition testing is superior to random testing and, consequently, to give further insights into the value of partition testing strategies.