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

Miltiadis Siavvas; Dimitrios Tsoukalas; Marija Jankovic; Dionysios Kehagias; Dimitrios Tzovaras

Vulnerability prediction facilitates the development of secure soft-ware, as it enables the identification and mitigation of security risks early enough in the software development lifecycle. Although sev-eral 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.

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Files are currently under embargo but will be publicly accessible after September 24, 2022.

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