Hybrid test data generation

Abstract

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.

Hybrid statistical estimation of mutual information and its application to information flow

“…The idea on combining static and randomized approaches to quantitative information flow was first proposed by Köpf and Rybalchenko [KR10] while our approach takes a different approach relying on statistical estimation to have better precision and accuracy and is general enough to deal with probabilistic systems under various prior information conditions. In related fields, the hybrid approach combining precise and statistical analysis have been proven to be effective, for instance in concolic analysis [MS07,LCFS14], where it is shown that input generated by hybrid techniques leads to greater code coverage than input generated by both fully random and concolic generation. After the publication of the preliminary version [KBL16] of this paper, a few papers on quantitative information flow combining symbolic and statistical approaches have been published.…”