比较机器学习算法预测被调查Bug的严重性和非严重性

2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) Pub Date : 2020-10-09 DOI:10.1109/ICSTCEE49637.2020.9276756

Muppala Sunny Chowdhary, R. Aishwarya, A. Abinay, Pasthupuram Harikrishna

{"title":"比较机器学习算法预测被调查Bug的严重性和非严重性","authors":"Muppala Sunny Chowdhary, R. Aishwarya, A. Abinay, Pasthupuram Harikrishna","doi":"10.1109/ICSTCEE49637.2020.9276756","DOIUrl":null,"url":null,"abstract":"Programming Bug announcing is a vital piece of the product advancement process. When a Bug is accounted for on the Bug detection, their qualities are broken down and along these lines relegated to different fixers for their goals. Bug severity, a characteristic of a product bug survey is the level of effect that imperfection has on the advancement or activity of a segment or framework. In this paper, an endeavour has been made to show the pertinence of AI calculations to be specific SVM, KNN, Naïve Bayes, Naïve Bayes Multinomial, J48 and RIPPER in order to find out which classification algorithm is accurate to find the bug severity. The pertinence of the calculation in deciding the different degrees of bug severity for bug storehouses have been approved utilizing different execution measures by applying 5-overlap cross-approval.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparing Machine-Learning Algorithms for Anticipating the Severity and Non-Severity of a Surveyed Bug\",\"authors\":\"Muppala Sunny Chowdhary, R. Aishwarya, A. Abinay, Pasthupuram Harikrishna\",\"doi\":\"10.1109/ICSTCEE49637.2020.9276756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Programming Bug announcing is a vital piece of the product advancement process. When a Bug is accounted for on the Bug detection, their qualities are broken down and along these lines relegated to different fixers for their goals. Bug severity, a characteristic of a product bug survey is the level of effect that imperfection has on the advancement or activity of a segment or framework. In this paper, an endeavour has been made to show the pertinence of AI calculations to be specific SVM, KNN, Naïve Bayes, Naïve Bayes Multinomial, J48 and RIPPER in order to find out which classification algorithm is accurate to find the bug severity. The pertinence of the calculation in deciding the different degrees of bug severity for bug storehouses have been approved utilizing different execution measures by applying 5-overlap cross-approval.\",\"PeriodicalId\":113845,\"journal\":{\"name\":\"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSTCEE49637.2020.9276756\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTCEE49637.2020.9276756","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

编程Bug发布是产品开发过程中至关重要的一部分。当一个Bug在Bug检测中被解释时，它们的质量被分解，并按照这些线被降级到不同的修复器中，以实现它们的目标。缺陷严重性，产品缺陷调查的一个特征是缺陷对一个部分或框架的进展或活动的影响程度。在本文中，我们试图展示AI计算对于SVM、KNN、Naïve Bayes、Naïve Bayes Multinomial、J48和RIPPER的针对性，以找出哪种分类算法更准确地发现漏洞的严重性。采用5重叠交叉审批的方法，采用不同的执行措施，验证了计算在确定bug库不同级别bug严重程度时的针对性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Comparing Machine-Learning Algorithms for Anticipating the Severity and Non-Severity of a Surveyed Bug

Programming Bug announcing is a vital piece of the product advancement process. When a Bug is accounted for on the Bug detection, their qualities are broken down and along these lines relegated to different fixers for their goals. Bug severity, a characteristic of a product bug survey is the level of effect that imperfection has on the advancement or activity of a segment or framework. In this paper, an endeavour has been made to show the pertinence of AI calculations to be specific SVM, KNN, Naïve Bayes, Naïve Bayes Multinomial, J48 and RIPPER in order to find out which classification algorithm is accurate to find the bug severity. The pertinence of the calculation in deciding the different degrees of bug severity for bug storehouses have been approved utilizing different execution measures by applying 5-overlap cross-approval.

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

2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)

自引率

0.00%

发文量