IGTG&R:一个意图分析引导的单元测试生成和改进框架
Xiaojian Liu1, Yangyang Zhang2
1College of Computer Science, Beijing University of Technology, Beijing 100124, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
一个新的框架,意图分析引导的单元测试生成和改进 (IGTG&R),改善了软件测试中的功能缺陷检测. 这种方法结合了代码覆盖导向的单元测试生成和基于大型语言模型的测试生成,以获得卓越的结果.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 自动化软件测试自动化软件测试
背景情况:
- 代码覆盖引导单元测试生成 (CGTG) 提供了高可执行性,但无法检测功能缺陷.
- 基于大型语言模型的测试生成 (LLMTG) 在意图分析方面表现出色,但其执行能力较低,需要调试.
- 现有的方法在全面识别功能缺陷,同时保持测试质量方面存在局限性.
研究的目的:
- 引入一个新的框架,意图分析引导的单元测试生成和改进 (IGTG&R) 模型,以提高单元测试生成中的功能缺陷的识别.
- 结合CGTG和LLMTG的优势,以提高代码意图理解和缺陷检测能力.
- 评估IGTG&R模型与传统CGTG和LLMTG方法的有效性.
主要方法:
- 在IGTG&R模型中采用了两阶段的测试生成过程.
- 第一个阶段使用覆盖路径来增强CGTG,以提高可执行性和代码覆盖率.
- 第二阶段改进了使用LLM用于功能缺陷识别的测试案例,不包括用于减少干扰的焦点方法体.
主要成果:
- IGTG&R模型实现了功能缺陷识别率在65%至89%之间.
- 该模型显示了100%的执行成功率和75.8%的代码覆盖率.
- 在识别功能缺陷和整体测试质量方面,IGTG&R的表现优于CGTG和LLMTG.
结论:
- 通过整合代码覆盖和意图分析,IGTG&R模型为自动化单元测试生成提供了显著的进步.
- 拟议的方法有效地解决了现有方法的局限性,从而实现更强大,更准确的缺陷检测.
- IGTG&R框架为软件测试提供了卓越的解决方案,增强了识别功能缺陷和确保代码质量的能力.
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