基于改进马尔可夫模型的软件测试数据自动生成

Web Intell. Pub Date : 2022-08-16 DOI:10.3233/web-220028
Jiali Chen, Xiaojie Chen, Tao Zan, Mengjia Lian
{"title":"基于改进马尔可夫模型的软件测试数据自动生成","authors":"Jiali Chen, Xiaojie Chen, Tao Zan, Mengjia Lian","doi":"10.3233/web-220028","DOIUrl":null,"url":null,"abstract":"In order to overcome the problems of low data reliability and long generation time of traditional automatic generation methods of software test data, an automatic generation method of software test data based on improved Markov model is designed. Firstly, collect software test data in different stages; Then, by calculating the similarity of the collected software test data, remove the test data with high similarity, calculate the importance of the software test data with the help of entropy weight method, and complete the data preprocessing; Finally, the Markov model is improved with the help of genetic algorithm, generation path and variation factor of software test data are set, and the improved Markov model is used to automatically generate high quality software test data. Experimental results show that when the number of experiments is 50, the generation time of this method is about 2.8 s, the reliability coefficient is always higher than 0.8.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An automatic generation of software test data based on improved Markov model\",\"authors\":\"Jiali Chen, Xiaojie Chen, Tao Zan, Mengjia Lian\",\"doi\":\"10.3233/web-220028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to overcome the problems of low data reliability and long generation time of traditional automatic generation methods of software test data, an automatic generation method of software test data based on improved Markov model is designed. Firstly, collect software test data in different stages; Then, by calculating the similarity of the collected software test data, remove the test data with high similarity, calculate the importance of the software test data with the help of entropy weight method, and complete the data preprocessing; Finally, the Markov model is improved with the help of genetic algorithm, generation path and variation factor of software test data are set, and the improved Markov model is used to automatically generate high quality software test data. Experimental results show that when the number of experiments is 50, the generation time of this method is about 2.8 s, the reliability coefficient is always higher than 0.8.\",\"PeriodicalId\":245783,\"journal\":{\"name\":\"Web Intell.\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Web Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/web-220028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Web Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/web-220028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

针对传统软件测试数据自动生成方法存在的数据可靠性低、生成时间长等问题,设计了一种基于改进马尔可夫模型的软件测试数据自动生成方法。首先,收集不同阶段的软件测试数据;然后,通过计算采集到的软件测试数据的相似度,剔除相似度高的测试数据,利用熵权法计算软件测试数据的重要度,完成数据预处理;最后,利用遗传算法对马尔可夫模型进行改进,设置软件测试数据的生成路径和变异因子,利用改进的马尔可夫模型自动生成高质量的软件测试数据。实验结果表明,当实验次数为50次时,该方法的生成时间约为2.8 s,可靠性系数始终大于0.8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An automatic generation of software test data based on improved Markov model
In order to overcome the problems of low data reliability and long generation time of traditional automatic generation methods of software test data, an automatic generation method of software test data based on improved Markov model is designed. Firstly, collect software test data in different stages; Then, by calculating the similarity of the collected software test data, remove the test data with high similarity, calculate the importance of the software test data with the help of entropy weight method, and complete the data preprocessing; Finally, the Markov model is improved with the help of genetic algorithm, generation path and variation factor of software test data are set, and the improved Markov model is used to automatically generate high quality software test data. Experimental results show that when the number of experiments is 50, the generation time of this method is about 2.8 s, the reliability coefficient is always higher than 0.8.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信