Output Sampling for Output Diversity in Automatic Unit Test Generation

Diverse test sets are able to expose bugs that generated with structural coverage techniques cannot discover. Input-diverse set generators have been shown be effective for this, but also limitations: e.g., they need complemented semantic information derived from the Software Under Test. We demonstrate how drive generation process in form of output diversity. present first totally automatic sampling diversity unit tool, called OutGen. OutGen transforms a program into an SMT formula bit-vector arithmetic. It then applies universal hashing order generate output-based diverse inputs. The result offers significant improvements when measured as high uniqueness count. achieves this by ensuring set’s probability distribution is uniform, i.e., highly diverse. use sampling, opposed any input CBMC, CAVM, behaviour or random testing improves mutation score and bug detection up 4150 963 percent respectively on programs drawn three different corpora: R-project, SIR CodeFlaws. achieve average 92 percent, 70 detect defect. Moreover, only tool real number C functions R-project.