in this paper, we have developed an approach to generate test data for path coverage based testing. The main challenge of this kind testing lies in its ability to build efficiently such a test suite in order to minimize the number of rejects. We address this problem with a novel divide-andconquer approach based on adaptive random testing strategy. Our approach takes as input the constraints of an executable path and computes a tight over-approximation of their associated sub-domain by using a dynamic domain partitioning approach. We implemented this approach and got experimental results that show the practical benefits compared to existing approaches. Our method generates less invalid inputs and is capable of obtaining the sub-domain of many complex constraints. Abstract-software testing; test data; path coverage; random testing; adaptive random testing.I.

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Path-oriented random testing aims at generating a uniformly distributed sequence of test data from a program input domain space to traverse a desired execution path of the program. To this aim, this article proposes a new algorithm to refine a program inputs domain space from invalid subdomains not covering the path. The validity of the subdomains is checked by a constraint propagation method against the path constraints (PCs). The proposed algorithm uses a divideand-conquer technique to iteratively split the inputs domain into subdomains and each time refutes those subdomains that are inconsistent with the PCs. The remaining shrunken subdomains provide all possible test data covering the desired path. Obviously, the more accurate the input domain is, the more effective test data will result. Experiments show the proposed method outperformed other related methods on a set of classical benchmark programs.

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