构建一个开源系统测试生成工具:用EvoMaster学习经验教训和经验分析
Andrea Arcuri1,2, Man Zhang1, Asma Belhadi1
1Kristiania University College, Oslo, Norway.
概括
开发人工智能驱动的软件测试工具带来了独特的挑战,特别是对于学术研究人员来说. 这篇论文分享了构建EvoMaster工具的经验,以帮助研究界和改善工业软件测试实践.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
背景情况:
- 软件测试研究经常需要开发原型工具.
- 研究人员通常缺乏工业经验,面临着开发,使用和验证的挑战.
- 由于它们固有的随机性,人工智能技术 (如进化算法) 出现了特定的问题.
研究的目的:
- 报告构建开源EvoMaster工具用于系统级测试案例生成的经验.
- 在研究软件原型中为共同的发展挑战提供具体的解决方案.
- 促进研究界的发展,增加科学研究对工业实践的影响.
主要方法:
- 开发开源工具EvoMaster的开发.
- 应用基于人工智能的技术,特别是进化算法,用于测试案例生成.
- 解决与软件架构相关的挑战,以实现高效的实验执行.
主要成果:
- 识别开发和验证软件测试原型的具体挑战.
- 成功开发了EvoMaster工具,用于企业应用程序系统级测试案例生成.
- 在经验研究中管理AI技术的随机性方面获得的经验.
结论:
- 在构建研究原型方面分享实践经验可以显著地使软件测试研究社区受益.
- 积极应对发展挑战会带来更强大,更有影响力的研究工具.
- 改进的研究原型可以弥合科学进步和工业软件测试实践之间的差距.
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