A framework of greedy methods for constructing interaction test suites
Proceedings of the 27th international conference on Software engineering
Test prioritization for pairwise interaction coverage
A-MOST '05 Proceedings of the 1st international workshop on Advances in model-based testing
One-test-at-a-time heuristic search for interaction test suites
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The test suite generation problem: Optimal instances and their implications
Discrete Applied Mathematics
Generating combinatorial test suite for interaction relationship
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
A backtracking search tool for constructing combinatorial test suites
Journal of Systems and Software
A survey of combinatorial testing
ACM Computing Surveys (CSUR)
Application of quotient space theory in input-output relationship based combinatorial testing
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
The Minimal Failure-Causing Schema of Combinatorial Testing
ACM Transactions on Software Engineering and Methodology (TOSEM)
Constraint-Based approaches to the covering test problem
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
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In this paper, we consider a problem that arises in blackbox testing: generating small test suites (i.e., sets of testcases) where the combinations that have to be covered arespecified by input-output parameter relationships of a softwaresystem. That is, we only consider combinations of inputparameters that affect an output parameter. We also donot assume that the input parameters have the same numberof values. To solve this problem, we revisit the greedy algorithmfor test generation and show that the size of the testsuite it generates is within a logarithmic factor of the optimal.Unfortunately, the algorithm's main weaknesses areits time and space requirements for construction. To addressthis issue, we present a problem reduction techniquethat makes the greedy algorithm or any other test suite generationmethod more efficient if the reduction in size is significant.