A path and branch based approach to fitness computation for program test data generation using genetic algorithm

Abstract

In this paper we present a novel approach for fitness computation for test data generation using genetic algorithm. Fitness computation is a two-step process. In the first step a target node sequence is determined and in the second step the actual execution path is compared with the target node sequence to compute fitness. Fitness computation uses both branch and path information. Experiments indicate that the described fitness technique results in significant improvement in search performanceKeywords-program test data generation, search-based software testing, genetic algorithm I.978-1-4799-8433-6/15/$31.00

Crow Search Algorithm with Improved Objective Function for Test Case Generation and Optimization

“…6 depicts the parameter setting of CSA for TIP and IAN. The values are based on some research work [3,16,18], general sources, and the nature of the programs. The system configuration is Windows 10, 8GB RAM, Intel Core i7,4690T central processing unit, 2.50 GHz, 64-bit OS, x64 based processor.…”

“…For developing test cases automatically, Search-Based Testing (SBT) is considered. There have been meta-heuristic techniques effectively utilized in the development of test data, including Genetic Algorithms (GA) [3,4], Ant Colony Optimization(ACO) [5,6], Particle Swarm Optimization (PSO) [7,8], Artificial Bee Colony (ABC) [9,10], hybrid Genetic Algorithm [11], Bat Algorithm [12,13]. Fitness functions (FF) drive a metaheuristic algorithm search process.…”