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Technical debt as an indicator of software security risk: a machine learning approach for software development enterprises.

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Title: Technical debt as an indicator of software security risk: a machine learning approach for software development enterprises.
Authors: Siavvas, Miltiadis, Tsoukalas, Dimitrios, Jankovic, Marija, Kehagias, Dionysios, Tzovaras, Dimitrios
Source: Enterprise Information Systems; May2022, Vol. 16 Issue 5, p1-43, 43p
Subject Terms: COMPUTER software security, COMPUTER software development, DEBT, SOFTWARE engineering, BUSINESS enterprises, SOFTWARE product line engineering, MACHINE learning
Abstract: Vulnerability prediction facilitates the development of secure software, as it enables the identification and mitigation of security risks early enough in the software development lifecycle. Although several factors have been studied for their ability to indicate software security risk, very limited attention has been given to technical debt (TD), despite its potential relevance to software security. To this end, in the present study, we investigate the ability of common TD indicators to indicate security risks in software products, both at project-level and at class-level of granularity. Our findings suggest that TD indicators may potentially act as security indicators as well. [ABSTRACT FROM AUTHOR]
Copyright of Enterprise Information Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: Technical debt as an indicator of software security risk: a machine learning approach for software development enterprises.
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  Data: Enterprise Information Systems; May2022, Vol. 16 Issue 5, p1-43, 43p
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  Data: <searchLink fieldCode="DE" term="%22COMPUTER+software+security%22">COMPUTER software security</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+software+development%22">COMPUTER software development</searchLink><br /><searchLink fieldCode="DE" term="%22DEBT%22">DEBT</searchLink><br /><searchLink fieldCode="DE" term="%22SOFTWARE+engineering%22">SOFTWARE engineering</searchLink><br /><searchLink fieldCode="DE" term="%22BUSINESS+enterprises%22">BUSINESS enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22SOFTWARE+product+line+engineering%22">SOFTWARE product line engineering</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink>
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  Data: Vulnerability prediction facilitates the development of secure software, as it enables the identification and mitigation of security risks early enough in the software development lifecycle. Although several factors have been studied for their ability to indicate software security risk, very limited attention has been given to technical debt (TD), despite its potential relevance to software security. To this end, in the present study, we investigate the ability of common TD indicators to indicate security risks in software products, both at project-level and at class-level of granularity. Our findings suggest that TD indicators may potentially act as security indicators as well. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Enterprise Information Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2022
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