Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
A survey of combinatorial testing
ACM Computing Surveys (CSUR)
A Formal Logic Approach to Constrained Combinatorial Testing
Journal of Automated Reasoning
Information Sciences: an International Journal
Evaluating improvements to a meta-heuristic search for constrained interaction testing
Empirical Software Engineering
A survey of methods for constructing covering arrays
Programming and Computing Software
The Minimal Failure-Causing Schema of Combinatorial Testing
ACM Transactions on Software Engineering and Methodology (TOSEM)
Test data regeneration: generating new test data from existing test data
Software Testing, Verification & Reliability
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Researchers have explored the application of combinatorial interaction testing (CIT) methods to construct samples to drive systematic testing of software system configurations. Applying CIT to highly-configurable software systems is complicated by the fact that, in many such systems, there are constraints between specific configuration parameters that render certain combinations invalid. In recent work, automated constraint solving methods have been combined with search-based CIT methods to address this problem with promising results. In this paper, we observe that the pattern of computation in greedy CIT algorithms leads to sequences of constraint solving problems that are closely related to one another. We propose two techniques for exploiting the history of constraint solving: (1) using incremental algorithms that are present within available constraint solvers and (2) mining constraint solver data structures to extract information that can be used to reduce the CIT search space. We evaluate the cost-effectiveness of these reductions on four real-world highly-configurable software systems and on a population of synthetic examples that share the characteristics of those four systems. In combination our techniques reduce the cost of CIT in the presence of constraints to that of traditional unconstrained CIT methods without sacrificing the quality of solutions.