An Empirical Analysis of Software Maintainability Using Code Quality and Complexity Metrics

Abstract

Software maintainability is a critical software quality attribute because the long-term cost of a software system is strongly influenced by the effort required to understand, modify, test, and evolve its source code. Increasing software size, structural complexity, code duplication, coupling, and code smells can make maintenance activities more difficult and may contribute to technical debt. This study proposes an empirical framework for examining the relationship between code quality and complexity metrics and software maintainability using software projects and source-code evolution data from 2020 to 2025. The study considers lines of code, cyclomatic complexity, cognitive complexity, code duplication, code smells, coupling, maintainability issues, technical debt, and maintainability rating as major variables. The proposed methodology combines static code analysis, descriptive statistics, correlation analysis, regression modelling, and comparative analysis of software releases. The framework is designed to identify the metrics that have the strongest association with maintainability outcomes. Recent research has demonstrated the relevance of code smells, complexity, architectural smells, and change-related metrics to software maintainability. In particular, recent empirical studies have examined code-smell impact, maintainability datasets, change-based prediction, and architectural smells. Wiley Online Library The proposed study provides a structured approach for evaluating maintainability and can support developers in prioritizing refactoring activities and reducing technical debt.