A Systematic Literature Review on Technical Debt in Software Development: Types, Tools, and Concerns

Software projects often accumulate technical debt, which undermines code quality, maintainability, and long-term viability. This article analyses twelve categories of technical debt and examines the capabilities of existing mitigation tools. To achieve this, we conducted a systematic literature review. Based on the results, we categorise and describe each debt type as it occurs in practice, and we assess prominent tools (such as AnaConDebt, CAST, DebtFlag, Visminer TD, and TD-Tracker) with respect to the debt types they target, the programming languages they support, and their adherence to established software quality models or methodologies. Our analysis shows that most current solutions focus on code and architectural debt, whereas documentation and process debt remain largely unaddressed. Moreover, most tools do not conform to software quality models or standards, which none of them explicitly incorporate.

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Journal
International Journal of Combinatorial Optimization Problems and Informatics.
Published
2025-10-12
DOI
https://doi.org/10.61467/2007.1558.2025.v16i4.1150
Primary Topic
Impact of AI and Big Data on Business and Society
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article
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article

A Systematic Literature Review on Technical Debt in Software Development: Types, Tools, and Concerns

Miguel Terrón-Hernández, Jezreel Mejía, Victor Terrón-Macias
International Journal of Combinatorial Optimization Problems and Informatics.
Impact of AI and Big Data on Business and Society
article

A Systematic Literature Review on Technical Debt in Software Development: Types, Tools, and Concerns

Miguel Terrón-Hernández, Jezreel Mejía, Victor Terrón-Macias
article en

Abstract

Software projects often accumulate technical debt, which undermines code quality, maintainability, and long-term viability. This article analyses twelve categories of technical debt and examines the capabilities of existing mitigation tools. To achieve this, we conducted a systematic literature review. Based on the results, we categorise and describe each debt type as it occurs in practice, and we assess prominent tools (such as AnaConDebt, CAST, DebtFlag, Visminer TD, and TD-Tracker) with respect to the debt types they target, the programming languages they support, and their adherence to established software quality models or methodologies. Our analysis shows that most current solutions focus on code and architectural debt, whereas documentation and process debt remain largely unaddressed. Moreover, most tools do not conform to software quality models or standards, which none of them explicitly incorporate.

International Journal of Combinatorial Optimization Problems and Informatics.Vol. 16(4)
Universidad Autónoma de Tlaxcala (MX), Mathematics Research Center (MX), Tecnológico de Monterrey (MX)
Openalex Percentile: Top 67%
Impact of AI and Big Data on Business and Society
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