{"title":"A New Method of Security Bug Reports Analysis","authors":"Yunwu Xu, Yan Li","doi":"10.1109/mitp.2023.3298520","DOIUrl":null,"url":null,"abstract":"The investigation develops a method for improving the quality of security bug report (SBR) prediction during the software development and application processes. The research includes three stages. The first stage is preparing the source data. The second stage is constructing an original SBR prediction method using a machine learning algorithm [random forest (RF)]. The third stage is evaluating our method with well-established methods like filtering and ranking for security bug report prediction (FARSEC) and Keywords Matrix. It was shown that the values of such indicators as accuracy, precision, recall, and F-score when using the RF algorithm are, on average, 0.2–1% higher than when using the FARSEC and Keywords Matrix methods. The more initial number of reports the database contains, the higher the value of accuracy, precision, recall, and F-score that can be obtained. A new method can be used to predict SBRs during the software development and application processes.","PeriodicalId":49045,"journal":{"name":"IT Professional","volume":"10 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IT Professional","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mitp.2023.3298520","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The investigation develops a method for improving the quality of security bug report (SBR) prediction during the software development and application processes. The research includes three stages. The first stage is preparing the source data. The second stage is constructing an original SBR prediction method using a machine learning algorithm [random forest (RF)]. The third stage is evaluating our method with well-established methods like filtering and ranking for security bug report prediction (FARSEC) and Keywords Matrix. It was shown that the values of such indicators as accuracy, precision, recall, and F-score when using the RF algorithm are, on average, 0.2–1% higher than when using the FARSEC and Keywords Matrix methods. The more initial number of reports the database contains, the higher the value of accuracy, precision, recall, and F-score that can be obtained. A new method can be used to predict SBRs during the software development and application processes.
IT ProfessionalCOMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
5.00
自引率
0.00%
发文量
111
审稿时长
>12 weeks
期刊介绍:
IT Professional is a technical magazine of the IEEE Computer Society. It publishes peer-reviewed articles, columns and departments written for and by IT practitioners and researchers covering:
practical aspects of emerging and leading-edge digital technologies,
original ideas and guidance for IT applications, and
novel IT solutions for the enterprise.
IT Professional’s goal is to inform the broad spectrum of IT executives, IT project managers, IT researchers, and IT application developers from industry, government, and academia.