Year: 2025
Type: dissertation
Abstract: Legacy code refactoring involves systematically restructuring outdated code bases to improve maintainability, readability, and extensibility while preserving existing functionality. Refactoring legacy code is a critical challenge in software engineering due to the accumulation of technical debt and the future cost incurred to repair the code. This research proposes a Transformer-based framework that combines Retrieval Augmented Generation with transformer-based language models and NSGA-II optimization for automated Python code refactoring. This research proposes a transformer-based framework combining Retrieval Augmented Generation with multi-objective optimization for automated Python code refactoring. The framework integrates multiple state-of-the-art language models through NSGA-II optimization to balance competing quality objectives. Results demonstrate significant improvements in refactoring quality and system efficiency. NSGAII optimization successfully identified Llama3-70B-8192 as the optimal model configuration, achieving significant improvements across multiple metrics: Answer Relevance (+2.00%), Response Completeness (+7.61%), and CodeBLEU Score (+69.23%). The optimization also improved system efficiency with reduced latency by 10.4% and decreased token usage by 24.9%. The framework achieved an overall 13.78% improvement in core quality metrics while maintaining 100% success rate across all test cases. This research aligns with the goals of Sustainable Development Goal 9 (SDG9), which emphasizes resilient infrastructure and sustainable industrial growth. This research benefits software development teams by reducing manual refactoring effort, organizations by minimizing code repair remediation costs, and society by enabling more resilient software infrastructure.
Source: NORMA
Author: Ravichandran, Shoban
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7163790806