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

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.