Test Case Generation and Reuse Based on Support Vector Machine Regression Model

QIAN Zhong-sheng; YU Qing-yuan; SONG Tao; ZHU Yi-min; ZHU Jie; ZHAO Chang

doi:10.12263/DZXB.20200426

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Test Case Generation and Reuse Based on Support Vector Machine Regression Model

PAPERS | 更新时间：2025-12-08

- Test Case Generation and Reuse Based on Support Vector Machine Regression Model
- ACTA ELECTRONICA SINICA Vol. 49, Issue 7, Pages: 1386-1391(2021)
- 作者机构：
  
  江西财经大学信息管理学院, 江西南昌 330013
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200426
  CLC： TP311
- Received：08 May 2020，
  
  Revised：2021-02-26，
  
  Published：25 July 2021
- 稿件说明：
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钱忠胜,俞情媛,宋涛等.基于支持向量机回归模型的测试用例生成与重用[J].电子学报,2021,49(07):1386-1391.

QIAN Zhong-sheng,YU Qing-yuan,SONG Tao,et al.Test Case Generation and Reuse Based on Support Vector Machine Regression Model[J].ACTA ELECTRONICA SINICA,2021,49(07):1386-1391.
钱忠胜,俞情媛,宋涛等.基于支持向量机回归模型的测试用例生成与重用[J].电子学报,2021,49(07):1386-1391. DOI： 10.12263/DZXB.20200426.

QIAN Zhong-sheng,YU Qing-yuan,SONG Tao,et al.Test Case Generation and Reuse Based on Support Vector Machine Regression Model[J].ACTA ELECTRONICA SINICA,2021,49(07):1386-1391. DOI： 10.12263/DZXB.20200426.

摘要

在软件测试领域

利用遗传算法生成测试用例是一个研究热点.传统方法在利用遗传算法生成测试用例时

需要计算每个个体的适应度.为了降低适应度计算的时间消耗并重用测试用例

提出一种融入支持向量机回归模型的测试用例生成与重用的方法.在使用遗传算法生成测试用例的过程中

利用一定数量的个体及其适应度作为样本训练支持向量机回归模型.在之后的种群进化中

根据回归模型计算个体适应度

同时利用回归模型查找适应度较高的个体并重用到新种群的进化中.在某大型程序实验中

该方法与同类经典方法相比

覆盖率提高了3%

平均进化代数也有所降低

其降低百分比达85.97%.

Abstract

In the field of software testing

it is a hot research spot to generate test cases using genetic algorithm. In the traditional process of generating test cases by genetic algorithm

it is necessary to calculate the fitness of each individual. In order to reduce the time consumption of fitness calculation and reuse test cases

a test case generation and reuse method based on support vector machine regression model is proposed. In the process of using genetic algorithm to generate test cases

a certain number of individuals and their fitness are used as samples to train the support vector machine regression model. In the subsequent population evolution

individual fitness is calculated according to the regression model. At the same time

individuals with higher fitness are found by the regression model and applied to the evolution of the new population. In the experiment on a large program

compared with that of the same classical method

the coverage rate of this approach is increased by 3% and the average evolutional time is also reduced by 85.97%.

关键词

Keywords

references

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