Paper Info
Title
Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic Algorithm
Abstract
Automatic path-oriented test data generation is an undecidable problem and genetic algorithm (GA) has been used to test data generation since 1992. In favor of MATLAB, a multi-population genetic algorithm (MPGA) was implemented, which selects individuals for free migration based on their fitness values. Applying MPGA to generating path-oriented test data generation is a new and meaningful attempt. After depicting how to transform path-oriented test data generation into an optimization problem, basic process flow of path-oriented test data generation using GA was presented. Using a triangle classifier as program under test, experimental results show that MPGA based approach can generate path-oriented test data more effectively and efficiently than simple GA based approach does.
Year
DOI
Venue
2008
10.1109/ICNC.2008.388
ICNC
Keywords
Field
DocType
automatic path-oriented test data,undecidable problem,genetic algorithm,simple ga,optimization problem,path-oriented test data generation,multi-population genetic algorithm,basic process flow,data generation,path-oriented test data,test data generation,software testing,testing,population genetics,gallium,control flow graph,genetic algorithms
Population,MATLAB,Control flow graph,Computer science,Algorithm,Artificial intelligence,Test data,Classifier (linguistics),Optimization problem,Machine learning,Genetic algorithm,Test data generation
Conference
Citations 
PageRank 
References 
19
0.93
12
Authors
2
Name
Order
Citations
PageRank
Yong Chen15212.67
Yong Zhong2242.71