Return to search

A hybrid genetic algorithm for automatic test data generation

Automatic test data generation is a hot topic in recent software testing research. Various techniques have been proposed with different emphases. Among them, most of the methods are based on Genetic Algorithms (GA). However, whether it is the best Metaheuristic method for such a problem remains unclear. In this paper, we choose to use another approach which arms a GA with an intensive local searcher (the so-called Memetic Algorithm (MA) according to the recent terminology). The idea of incorporating local searcher is based on the observations from many real-world programs. It turns out the results outperform many other known Metaheuristic methods so far. We argue the needs of local search for software testing in the discussion of the paper.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0713106-201239
Date13 July 2006
CreatorsWang, Hsiu-Chi
ContributorsChia-Mei Chen, Sheng-Tzong Cheng, Yuh-Jiuan Tsay, Bingchiang Jeng
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0713106-201239
Rightsunrestricted, Copyright information available at source archive

Page generated in 0.0019 seconds