Technical Debt in Software Quality Engineering: A Quantitative Model for Risk Mitigation

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Published: Mar 31, 2023
Keywords:
Technical debt, risk mitigation, software maintainability, defect density, Agile, DevOps, risk assessment models, software quality, automated monitoring, artificial intelligence in software engineering

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Gopinath Kathiresan
Senior Quality Engineering Manager, CA, USA

Abstract

Technical debt is now one of the significant challenges pertaining to modern software development, as they affect software maintainability, scalability, and future costs. This paper discusses a quantitative approach toward reducing an accumulated technical debt using risk assessment models, automated monitoring, and software engineering best practices. The study set up a structured framework of measuring and prioritizing technical debt based on defect density, code complexity, and technical debt ratio analysis. Real industry case studies demonstrate the visible and unarguable effectiveness of risk-based application strategies toward improvements in software quality and sustainability of a project. In addition, the paper proposes future research directions based on the results obtained about contributions from AI and automation in the area of technical debt management. It was discovered that integrating proactive risk mitigation strategies with Agile-and-DevOps workflow could significantly improve the software development process.

Article Details

How to Cite
Kathiresan, G. (2023). Technical Debt in Software Quality Engineering: A Quantitative Model for Risk Mitigation. CINEFORUM, 63(1), 71–88. Retrieved from https://revistadecineforum.com/index.php/cf/article/view/269
Issue
Vol. 63 No. 1 (2023)
Section
Original Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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