Code Smell Severity in Predictive Modeling for Software Quality Risk and Maintenance Effort: A Systematic Literature Review

Abstract

Code smells are structural indicators of poor soft-ware design that have been empirically associated with higher defect rates and increased maintenance effort. Although research on smell detection, software quality risk, and maintenance effort estimation has grown significantly, these areas have largely been studied separately. In addition, the role of smell severity in predictive modeling remains insufficiently synthesized. This Systematic Literature Review (SLR) investigates empirical studies published between 2010 and 2025, while also including selected foundational studies where appropriate. A total of 38 studies were selected for analysis through a structured search and screening process. The review addresses two research questions: 1) how smell type and severity influence software quality risk and maintenance effort, and 2) the extent to which current predictive approaches incorporate smell-related information in software defect prediction. The findings show that design-level smells are more consistently associated with fault-proneness than method-level smells. The review also finds that severity-aware models generally outperform binary detection models. However, the independent effect of code smells on directly measured maintenance effort remains inconsistent when confounding factors such as code size and code churn are taken into account. Furthermore, fully unified models that jointly predict defect risk and maintenance effort using smell severity as structured input remain largely absent. Overall, these findings highlight the need for standardized severity operationalization, integrated datasets, and interpretable unified modeling frameworks to advance software quality analysis.