An Improved Genetic Algorithm for Test Cases Generation Oriented Paths

MEI Jia; WANG Shengyuan

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(3): 494-498

MEI Jia and WANG Shengyuan, “An Improved Genetic Algorithm for Test Cases Generation Oriented Paths,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 494-498, 2014,

Citation:

MEI Jia and WANG Shengyuan, “An Improved Genetic Algorithm for Test Cases Generation Oriented Paths,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 494-498, 2014,

MEI Jia and WANG Shengyuan, “An Improved Genetic Algorithm for Test Cases Generation Oriented Paths,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 494-498, 2014,

Citation:

MEI Jia and WANG Shengyuan, “An Improved Genetic Algorithm for Test Cases Generation Oriented Paths,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 494-498, 2014,

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An Improved Genetic Algorithm for Test Cases Generation Oriented Paths

Department of Computer Science and Technology,Tsinghua University, Beijing 100084, China

Funds: This work is supported in part by National Natural Science Foundation of China (No.61272086, No.61170051), Guangxi Nature Science Fund (No.2012jjBAG0074), Guangxi Education Department Scientific Research Items (No.201106LX840), Guangxi Key laboratory of hybrid computation and IC design analysis Open Foundation (No.2012HCIC05), and National Social Science Fund (No.11CTQ008).

Received Date: 2013-12-01
Rev Recd Date: 2014-02-01
Publish Date: 2014-07-05

Abstract

Abstract

This paper discusses a method that can automatically generate test cases for selected paths using a special genetic algorithm. The special algorithm is called Queen-bee evolutionary genetic algorithm(QBEA). In this algorithm, sequences of operators iteratively executes for test cases to evolve to target paths. The best chromosome called queen among the current population is crossover with drones selected according to a certain crossover probability, which enhances the exploitation of searching global optimum. A comparative experiment results prove that the proposed method is actually a great improvement in optimization efficiency and optimization effect.
- Software testing,
- Test data generation,
- Queen-bee evolutionary genetic algorithm

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References(23)

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