Generation Method of Class Integration Test Order Based on Deep Reinforcement Learning

ZHANG Ying-hui; ZHANG Yan-mei; ZHANG Zhi-cheng; JIANG Shu-juan; DING Yan-ru; YUAN Guan

doi:10.12263/DZXB.20210688

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Generation Method of Class Integration Test Order Based on Deep Reinforcement Learning

PAPERS | 更新时间：2025-12-08

- Generation Method of Class Integration Test Order Based on Deep Reinforcement Learning
- ACTA ELECTRONICA SINICA Vol. 51, Issue 2, Pages: 455-466(2023)
- 作者机构：
  
  1.中国矿业大学矿山数字化教育部工程研究中心，江苏徐州 221116
  2.中国矿业大学计算机科学与技术学院，江苏徐州 221116
  3.广西可信软件重点实验室（桂林电子科技大学），广西桂林541004
  4.南方科技大学工学院计算机科学与工程系，广东深圳 518055
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61673384;71774159);the Postdoctoral Foundation of China(2021T140707);the Guangxi Trusted Software Key Laboratory(kx201609)
- DOI：10.12263/DZXB.20210688
  CLC： TP311;
- Received：08 May 2021，
  
  Revised：2022-05-31，
  
  Published：25 February 2023
- 稿件说明：
移动端阅览
张颖辉,张艳梅,张志成等.基于深度强化学习的类集成测试序列生成方法[J].电子学报,2023,51(02):455-466.

ZHANG Ying-hui,ZHANG Yan-mei,ZHANG Zhi-cheng,et al.Generation Method of Class Integration Test Order Based on Deep Reinforcement Learning[J].ACTA ELECTRONICA SINICA,2023,51(02):455-466.
张颖辉,张艳梅,张志成等.基于深度强化学习的类集成测试序列生成方法[J].电子学报,2023,51(02):455-466. DOI： 10.12263/DZXB.20210688.

ZHANG Ying-hui,ZHANG Yan-mei,ZHANG Zhi-cheng,et al.Generation Method of Class Integration Test Order Based on Deep Reinforcement Learning[J].ACTA ELECTRONICA SINICA,2023,51(02):455-466. DOI： 10.12263/DZXB.20210688.

摘要

类集成测试序列的生成是面向对象软件测试中的关键步骤，当类的测试序列不同时，相应的测试代价也不相同.在集成测试中生成一个合理的类集成测试序列可以有效降低软件测试的代价.本文将深度强化学习中的Advantage Actor-Critic算法应用于解决类集成测试序列生成问题.首先，利用类间各种依赖关系构建与智能体交互的环境模型；然后，记录智能体从初始状态到终止状态的路径，即每次选择的动作对应每次选择集成到序列的类编号；最后，得出最终的类集成测试序列.实验结果表明，本文方法所得到的类集成测试序列花费的总体测试桩复杂度，在选取的7个项目中有5个表现最佳，在剩余2个项目中表现中等.

Abstract

The generation of class integration test order is the key step in object-oriented software testing. When the class integration test order is different

the corresponding test cost is different. Generating a reasonable class integration test order in integration testing can effectively reduce the cost of software testing. This paper applies the advantage actor-critic algorithm in deep reinforcement learning to solve the problem of class integration test order generation. Firstly

the environment model of interaction with agents is constructed by using various dependencies between classes. Then

the path of the agent from the initial state to the termination state is recorded

that is

each selected action corresponds to each selected class number integrated into the order. Finally

the final class integration test order is obtained. The experimental results show that the total test stubs complexity of class integration test order cost obtained by the method in this paper has the best performance in 5 out of 7 selected subjects

and the average performance in the remaining 2 subjects.

关键词

Keywords

references

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Related Institution

Department of Computer Science and Engineering， College of Engineering， Southern University of Science and Technology

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