A Comparative Analysis on Code Smell Refactoring Sequencing for Object-Oriented Systems using Hybrid Optimization Approaches DOI

Ritika,

Navdeep Kaur, Amandeep Kaur

et al.

Published: May 2, 2025

Abstract Code smells are indicators of potential design flaws in object-oriented systems that can lead to maintenance challenges, reduced performance, and increased technical debt. Refactoring these is essential improving software quality. However, the process sequencing refactorings efficiently remains a complex optimization problem. This systematic review explores role hybrid approaches automating enhancing code smell refactoring sequences. We analyse existing research on strategies, highlighting how heuristic, metaheuristic, machine learning-based techniques have been combined optimize decisions. Various models such as genetic algorithms, particle swarm optimization, ant colony deep learning proposed balance maintainability, modularity, performance. Our study categorizes methods based their effectiveness detecting mitigating different types smells, including long methods, large classes, feature envy. also discuss empirical evaluations compare approaches, shedding light strengths limitations. provides comprehensive synthesis recent advancements identifies future directions.

Language: Английский

A Comparative Analysis on Code Smell Refactoring Sequencing for Object-Oriented Systems using Hybrid Optimization Approaches DOI

Ritika,

Navdeep Kaur, Amandeep Kaur

et al.

Published: May 2, 2025

Abstract Code smells are indicators of potential design flaws in object-oriented systems that can lead to maintenance challenges, reduced performance, and increased technical debt. Refactoring these is essential improving software quality. However, the process sequencing refactorings efficiently remains a complex optimization problem. This systematic review explores role hybrid approaches automating enhancing code smell refactoring sequences. We analyse existing research on strategies, highlighting how heuristic, metaheuristic, machine learning-based techniques have been combined optimize decisions. Various models such as genetic algorithms, particle swarm optimization, ant colony deep learning proposed balance maintainability, modularity, performance. Our study categorizes methods based their effectiveness detecting mitigating different types smells, including long methods, large classes, feature envy. also discuss empirical evaluations compare approaches, shedding light strengths limitations. provides comprehensive synthesis recent advancements identifies future directions.

Language: Английский

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