Automated test data generation based on improved adaptive genetic algorithm HCGA

Jie Min Wang; Gang Yi Ding; Han Tao Song; Jian Guo Xiong

Automated test data generation based on improved adaptive genetic algorithm HCGA

Jie Min Wang^*, Gang Yi Ding, Han Tao Song, Jian Guo Xiong

^*此作品的通讯作者

计算机学院

科研成果: 期刊稿件 › 文章 › 同行评审

5 引用（Scopus）

摘要

An improved algorithm HCGA is proposed here based on the combination of adaptive genetic algorithm and hill climbing method for automated software test data generation. Adaptive crossover operator and mutation operator are designed to enhance the global search capability of genetic algorithm at starting. Afterwards hill climbing method is embedded to enhance the local search capability. Test examples show that it is better than genetic algorithm and can improve the efficiency of automated test data generation.

源语言	英语
页（从-至）	883-885+910
期刊	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
卷	27
期	10
出版状态	已出版 - 10月 2007

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引用此

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abstract = "An improved algorithm HCGA is proposed here based on the combination of adaptive genetic algorithm and hill climbing method for automated software test data generation. Adaptive crossover operator and mutation operator are designed to enhance the global search capability of genetic algorithm at starting. Afterwards hill climbing method is embedded to enhance the local search capability. Test examples show that it is better than genetic algorithm and can improve the efficiency of automated test data generation.",

keywords = "Adaptive genetic algorithm, Hill climbing method, Software testing, Test data generation",

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AU - Ding, Gang Yi

AU - Song, Han Tao

AU - Xiong, Jian Guo

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N2 - An improved algorithm HCGA is proposed here based on the combination of adaptive genetic algorithm and hill climbing method for automated software test data generation. Adaptive crossover operator and mutation operator are designed to enhance the global search capability of genetic algorithm at starting. Afterwards hill climbing method is embedded to enhance the local search capability. Test examples show that it is better than genetic algorithm and can improve the efficiency of automated test data generation.

AB - An improved algorithm HCGA is proposed here based on the combination of adaptive genetic algorithm and hill climbing method for automated software test data generation. Adaptive crossover operator and mutation operator are designed to enhance the global search capability of genetic algorithm at starting. Afterwards hill climbing method is embedded to enhance the local search capability. Test examples show that it is better than genetic algorithm and can improve the efficiency of automated test data generation.

KW - Adaptive genetic algorithm

KW - Hill climbing method

KW - Software testing

KW - Test data generation

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Automated test data generation based on improved adaptive genetic algorithm HCGA

摘要

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