多目标约束下软件故障检测与定位的测试套件优化:基于混合优化的模型

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Adline Freeda R, Selvi Rajendran P
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引用次数: 0

摘要

测试和调试一直是软件开发中最重要的步骤,因为工程师很难创建无错误的软件。软件测试发生在编码之后,目的是发现缺陷。如果发现错误,将进行调试以确定错误的来源,以便对其进行修复。因此,检测和定位缺陷是软件创建中的两个基本阶段。我们使用以下两个工作阶段创建了一种独特的方法,以生成能够检测和定位故障的最小化测试套件。在初始测试套件最小化过程中,使用提出的蓝猴定制黑寡妇(BMCBW)算法,根据D-score和覆盖率等目标生成和最小化用例。在最小化测试套件之后,进行故障验证,包括故障检测和定位过程。对于这种故障验证,我们使用了改进的长短期记忆(LSTM)。在90%的学习率下,所提出的工作的准确率分别为0.97%、2.20%、2.52%、0.97%和2.81%,优于现有的AOA、COOT、BES、BMO和BWO方法。实验结果表明,基于蓝猴定制黑寡妇优化的故障检测与定位方法具有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model
Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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