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Many automatic test data generation techniques have been proposed in the past decades. Each technique can only deal with very restrictive data types so far. This limits the usefulness of test data generation in practice. We present a preliminary approach on hybrid test data generation, by combining Random Strategy (RS), Dynamic Symbolic Execution (DSE), and Search-based Strategy (SBS). It is expected to take advantage of the state-of-the-arts to enhance the robustness and scalability, in terms of different types of test data.

Abstract-Symbolic execution maintains a path condition pc for every possible path of a program. It is challenging to construct a pc if some complex issues are involved in the path. A predicate interpretation pi is a subexpression of a pc and a pc of a path is a conjunction of all pis in the path. Predicate interpretation has been widely used in theoretical analysis on domain testing and related fields. It recently emerges new impact on software testing by using partial path constraints to generate test data. In this paper, we propose an approach to produce pis in a program. A tool for predicate interpretation analysis for Java programs is implemented based on the data-flow framework of Soot. Most of Java features can be handled in our tool. Moreover, a formal rule of predicate interpretation analysis is presented for more applications in the future. The experimental results show that our tool can produce pis of a program effectively and efficiently.