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Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2467-2473

Algorithm for path coverage test data generation based on reinforcement learning selection strategy

Liu Chaoa
Ding Ruib
Zhu Yuhana
a. School of Mathematics & Science, b. School of Computing & Information Technology, Mudanjiang Normal University, Mudanjiang Heilongjiang 157000, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0592

Abstract

Path-coverage oriented testing is a crucial method in software testing, and the rapid generation of high-quality test data to satisfy path coverage requirements has been a persistent research challenge. To address issues such as long running times, unstable exploration processes, and the generation of redundant test cases in existing intelligent optimization methods, this paper proposed a selection strategy based on the reinforcement learning paradigm applied to test data generation with path coverage as the criterion. By defining executable paths as the state of the intelligent agent, it defined the data selection after each iteration update as the agent's action. It associated the reward function with state changes, and employed a greedy strategy during the state update process to guide input data towards continuous variations in unexplored states. This iterative selection process aimed to continuously choose data that covered new executable paths, thereby achieving the goal of covering all execution paths of the target program. Experimental results demonstrate that compared to other algorithms, the proposed strategy significantly reduces running times and iteration counts while achieving notable improvements in coverage. Theoretical ana-lysis supports the conclusion that the proposed strategy effectively realizes path coverage and enhances the efficiency of test data generation in practical applications.

Foundation Support

牡丹江师范学院资助项目(MNUGP202304,kjcx2022-020mdjnu,1451TD003)
黑龙江省自然科学基金资助项目(LH2023F037)
黑龙江省高等教育教学改革重点委托项目(SJGZ20200175)
黑龙江省高等教育教学改革项目(SJGY20220607)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0592
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Software Technology Research
Pages: 2467-2473
Serial Number: 1001-3695(2024)08-031-2467-07

Publish History

[2024-03-01] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

刘超, 丁蕊, 朱雨寒. 基于强化学习选择策略的路径覆盖测试数据生成算法 [J]. 计算机应用研究, 2024, 41 (8): 2467-2473. (Liu Chao, Ding Rui, Zhu Yuhan. Algorithm for path coverage test data generation based on reinforcement learning selection strategy [J]. Application Research of Computers, 2024, 41 (8): 2467-2473. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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