Global Trends and Empirical Metrics in the Evaluation of Code Smells and Technical Debt: A Bibliometric Study. | Zendy

Ronald Diaz-Arrieta | Zendy; Byron Diaz-Monroy | Zendy; Luis Castillo-Heredia | Zendy; Alexandra Valenzuela-Cobos | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Global Trends and Empirical Metrics in the Evaluation of Code Smells and Technical Debt: A Bibliometric Study.

Author(s) -

Ronald Diaz-Arrieta,

Byron Diaz-Monroy,

Luis Castillo-Heredia,

Alexandra Valenzuela-Cobos

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3594657

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Software quality and long-term maintainability represent fundamental challenges in modern software engineering. Among the critical factors that affect these attributes are code smells, indicators of structural deficiencies in the source code, which, although they do not directly affect functionality, significantly increase technical debt and maintenance costs. This study presents a comprehensive bibliometric analysis of the scientific literature published between 2020 and 2024, with the objective of identifying the main trends, authors, sources, metrics and mitigation techniques associated with code smells and their relationship with technical debt. Seventy-eight articles extracted from the Scopus and Web of Science databases were analyzed using Bibliometrix (R), Biblioshiny and VOSviewer tools, applying productivity indicators, co-authorship networks, term co-occurrence analysis and thematic evolution. The results reveal a growing annual scientific production (+35.79%), a concentration of publications in Q1 journals such as Journal of Systems and Software and IEEE Transactions on Software Engineering, and a strong presence of key authors such as Fabio Palomba and Tomas Cerny. The most prominent topics include the application of artificial intelligence techniques for automated detection of code smells, the use of empirical metrics such as cyclomatic complexity and data dependencies, and the implementation of strategies such as automated refactoring and peer review for mitigation. In addition, research gaps were identified in the evaluation of emerging code smells in modern architectures such as microservices or scientific software. This study not only consolidates existing knowledge, but also proposes new lines of research aimed at improving software sustainability through the use of predictive models and hybrid techniques. The bibliometric evidence obtained provides a solid framework for researchers and practitioners interested in optimizing code quality and proactively managing technical debt.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research