软件测试自动化:面向智能制造的开源自动化软件测试工具生产率比较研究

A. Sivaji, Rosnisa Abdull Razak, N. Mohamad, Nurshakirin Sazali, Afiqah Musa, Norzamzarini Bajuri, A. M. Hashim, Mohd Solehudin Abdullah, Nur Diyana Joha, Nadia Ellyani Azis, Anjana Devi N Kuppusamy, Azlan Deniel, N. Chuan, Torkil Clemmensen
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引用次数: 1

摘要

随着新兴技术的发展,手工软件测试在测试计划、测试用例创建、执行和报告方面对项目团队提出了挑战。使用开源工具的软件测试自动化承诺为软件测试工程师节省生产力。本研究的目的是比较用于web应用程序测试的开源智能制造工具包的生产力水平。结合文献分析,我们采访了软件测试领域的主题专家,并要求他们对软件开发生命周期中不同步骤中使用的两种工具的生产率进行实验和比较。在安装、脚本编写、验证和维护web应用程序测试方面,Katalon Studio 7.0被认为是一个比它的前身Robot Framework 3.0更智能、更高效的工具。通过使用Wilcoxon matching Pairs sign - rank检验,结果显示“在使用RF和KS执行软件测试自动化的时间方面,中小企业的生产率在统计上没有显著差异”的原假设可以被拒绝,Z= - 6.399, p < 0.05。未来的工作可以复制类似的研究,以选择适合和提高生产率的移动制造工具包。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Software Testing Automation: A Comparative Study on Productivity Rate of Open Source Automated Software Testing Tools For Smart Manufacturing
With the growth in emerging technologies, manual software testing possesses a challenge to project team in terms of test planning, test case creation, execution and reporting. Software testing automation with open source tools promises productivity savings for software test engineers. The objective of this study is to compare the level of productivity of open source Smart Manufacturing toolkits for web application testing. Together with literature analysis, subject matter experts in software testing were interviewed and were requested to experiment and compare the productivity rate of two tools in use for the various steps in the software development lifecycle. Katalon Studio 7.0 was found to be a more smart and productive tool than its predecessor, Robot Framework 3.0 in terms of installation, scripting, verification and maintenance for web application testing. By employing a Wilcoxon Matched Pairs Signed-Rank Test, results revealed that the null hypothesis of “ no statistically significant differences between SME productivity rate in using RF and KS to performing software test automation in terms of time taken” can be rejected , Z= −6.399, p < 0.05. Future work could replicate similar studies for selecting mobile manufacturing toolkits that will be suitable and improve productivity rate.
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