Test Data Generation of Path Coverage Based on Negative Selection Genetic Algorithm

XIA Chun-yan; ZHANG Yan; WAN Li; SONG Yan; XIAO Nan; GUO Bing

doi:10.3969/j.issn.0372-2112.2019.12.024

您当前的位置：

首页 >

文章列表页 >

Test Data Generation of Path Coverage Based on Negative Selection Genetic Algorithm

更新时间：2025-07-16

- Test Data Generation of Path Coverage Based on Negative Selection Genetic Algorithm
- Acta Electronica Sinica Vol. 47, Issue 12, Pages: 2630-2638(2019)
- 作者机构：
  
  1. 牡丹江师范学院计算机与信息技术学院,黑龙江,牡丹江,157012
  2. 天津大学智能与计算学部,天津,300350
  3. 牡丹江师范学院计算机与信息技术学院,黑龙江,牡丹江,157012
  4. 天津大学智能与计算学部,天津,300350
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.12.024
  CLC： TP311
- Published Online：25 December 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
XIA Chun-yan, ZHANG Yan, WAN Li, et al. Test Data Generation of Path Coverage Based on Negative Selection Genetic Algorithm[J]. Acta Electronica Sinica, 2019, 47(12): 2630-2638.
DOI：

XIA Chun-yan, ZHANG Yan, WAN Li, et al. Test Data Generation of Path Coverage Based on Negative Selection Genetic Algorithm[J]. Acta Electronica Sinica, 2019, 47(12): 2630-2638. DOI： 10.3969/j.issn.0372-2112.2019.12.024.

摘要

路径覆盖是软件测试领域重要的测试方法之一.在搜索空间中，找到一组测试数据满足路径覆盖是一个具有挑战性的问题.因此，自动生成测试数据是软件测试的关键问题.文中提出一种基于否定选择遗传算法的路径覆盖测试数据生成方法，将否定选择策略融入遗传算法，动态优化遗传算法的种群数据，自动生成覆盖目标路径的测试数据.多个基准程序和工业程序的实验结果表明，与随机方法和遗传算法比较，文中方法能够提高路径覆盖率，减少冗余测试数据的生成.

Abstract

Path coverage is one of the most important testing methods in the field of software testing. It is a challenging problem to find a set of test data to satisfy the path coverage in the search space. Therefore

automatically generating test data is a key issue in software testing. In this paper

a generation method of test data based on the negative selection genetic algorithm is proposed. The negative selection strategy is integrated into the genetic algorithm

and the population data of the genetic algorithm is dynamically optimized

and the test data covering the target path is automatically generated. The experimental results show that compared with the random method and the genetic algorithm

the proposed method can improve the path coverage and reduce the generation of redundant test data.

关键词

Keywords

references

Views

115

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Evolutionary Generation of Test Data for Paths Coverage Based on Balance Optimization Theory

Novel Evolutionary Generation Approach to Test Data for Multiple Paths Coverage

Evolutionary Generation of Test Data for Path Coverage Based on Automatic Reduction of Search Space

Mutation Testing Based on Comparison of Paths

Test Data Generation Based on Automatic Division of Path

Related Author

FAN Shu-ping

MA Bao-ying

WAN li

YAO Nian-min

FONT face

Verdana

GONG Dun-wei

ZHANG Yan

Related Institution

School of Health Management, Mudanjiang Medical University

Department of Intelligence and Computing, Tianjin University

School of Computer Science and Technology, Dalian University of Technology

School of Health Management Mudanjiang Medical University Mudanjiang Heilongjiang China

Department of Intelligence and Computing Tianjin University Tianjin China

⁰