An adaptive fitness function based on branch hardness for search based testing
Xiong Xu, Ziming Zhu, Li Jiao · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
Search based software testing has received great attention as a means of automating the test data generation, and the goal is to improve various criteria. There are different types of coverage criteria. In this paper, we deal with the path coverage. Concretely, we focus on the path that is the most difficult to cover. One major limitation of search based testing is the inefficient and insufficiently informed fitness function. To address this problem, we propose an adaptive fitness function based on branch hardness. The branch hardness is measured by the expected number of visits of each branch in the program, which is modeled by an absorbing discrete time Markov chain. By tuning the parameters of branch hardness heuristically, the search hardness, evaluated by the variation coefficient of the fitness function, of generating test data can be minimized. Therefore, this new fitness function is more flexible than the traditional counterparts. In addition, we point out that the present definition of branch distance and the use of normalizing functions are problematic, and propose some improvements. Finally, the empirical study reveals the promising result of our proposal in this paper.