No abstract

Combinatorial testing aims at reducing the cost of software and system testing by reducing the number of test cases to be executed. We propose an approach for combinatorial testing that generates a set of test cases that is as small as possible, using incremental SAT solving. We present several search-space pruning techniques that further improve our approach. Experiments show a significant improvement of our approach over other SAT-based approaches, and considerable reduction of the number of test cases over other combinatorial testing tools.

QC 20170117

Combinatorial testing aims at covering the interactions of parameters in a system under test, while some combinations may be forbidden by given constraints (forbidden tuples).In this paper, we illustrate that such forbidden tuples correspond to unsatisfiable cores, a widely understood notion in the SAT solving community. Based on this observation, we propose a technique to detect forbidden tuples lazily during a greedy test case generation, which significantly reduces the number of required SAT solving calls. We further reduce the amount of time spent in SAT solving by essentially ignoring constraints while constructing each test case, but then "amending" it to obtain a test case that satisfies the constraints, again using unsatisfiable cores. Finally, to complement a disturbance due to ignoring constraints, we implement an efficient approximative SAT checking function in the SAT solver Lingeling.Through experiments we verify that our approach significantly improves the efficiency of constraint handling in our greedy combinatorial testing algorithm.

No abstract

No abstract