Building an open-source system test generation tool: lessons learned and empirical analyses with EvoMaster.

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Quality Journal Pub Date : 2023-01-01 Epub Date: 2023-03-06 DOI:10.1007/s11219-023-09620-w
Andrea Arcuri, Man Zhang, Asma Belhadi, Bogdan Marculescu, Amid Golmohammadi, Juan Pablo Galeotti, Susruthan Seran
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引用次数: 0

Abstract

Research in software testing often involves the development of software prototypes. Like any piece of software, there are challenges in the development, use and verification of such tools. However, some challenges are rather specific to this problem domain. For example, often these tools are developed by PhD students straight out of bachelor/master degrees, possibly lacking any industrial experience in software development. Prototype tools are used to carry out empirical studies, possibly studying different parameters of novel designed algorithms. Software scaffolding is needed to run large sets of experiments efficiently. Furthermore, when using AI-based techniques like evolutionary algorithms, care needs to be taken to deal with their randomness, which further complicates their verification. The aforementioned represent some of the challenges we have identified for this domain. In this paper, we report on our experience in building the open-source EvoMaster tool, which aims at system-level test case generation for enterprise applications. Many of the challenges we faced would be common to any researcher needing to build software testing tool prototypes. Therefore, one goal is that our shared experience here will boost the research community, by providing concrete solutions to many development challenges in the building of such kind of research prototypes. Ultimately, this will lead to increase the impact of scientific research on industrial practice.

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构建开源系统测试生成工具:EvoMaster 的经验教训和实证分析。
软件测试研究通常涉及软件原型的开发。与任何软件一样,这些工具的开发、使用和验证都面临着挑战。然而,有些挑战是这一问题领域所特有的。例如,这些工具往往是由刚刚获得学士/硕士学位的博士生开发的,他们可能缺乏软件开发方面的工业经验。原型工具用于开展实证研究,可能是研究新设计算法的不同参数。要高效地运行大量实验,就需要软件支架。此外,在使用基于人工智能的技术(如进化算法)时,需要注意处理其随机性,这使其验证变得更加复杂。上述是我们在这一领域发现的一些挑战。在本文中,我们报告了我们在构建开源 EvoMaster 工具方面的经验,该工具旨在为企业应用程序生成系统级测试用例。我们面临的许多挑战对于任何需要构建软件测试工具原型的研究人员来说都是共同的。因此,我们的一个目标是,通过为构建此类研究原型过程中的许多开发挑战提供具体的解决方案,我们在此分享的经验将促进研究界的发展。最终,这将提高科学研究对工业实践的影响。
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来源期刊
Software Quality Journal
Software Quality Journal 工程技术-计算机:软件工程
CiteScore
4.90
自引率
5.30%
发文量
26
审稿时长
>12 weeks
期刊介绍: The aims of the Software Quality Journal are: (1) To promote awareness of the crucial role of quality management in the effective construction of the software systems developed, used, and/or maintained by organizations in pursuit of their business objectives. (2) To provide a forum of the exchange of experiences and information on software quality management and the methods, tools and products used to measure and achieve it. (3) To provide a vehicle for the publication of academic papers related to all aspects of software quality. The Journal addresses all aspects of software quality from both a practical and an academic viewpoint. It invites contributions from practitioners and academics, as well as national and international policy and standard making bodies, and sets out to be the definitive international reference source for such information. The Journal will accept research, technique, case study, survey and tutorial submissions that address quality-related issues including, but not limited to: internal and external quality standards, management of quality within organizations, technical aspects of quality, quality aspects for product vendors, software measurement and metrics, software testing and other quality assurance techniques, total quality management and cultural aspects. Other technical issues with regard to software quality, including: data management, formal methods, safety critical applications, and CASE.
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