Enhancing software quality attributes through multi-dimensional refactoring at source-level

Abstract

Cyber-Physical Systems (CPSs) increasingly depend on complex, high-level software components for coordination, integration, and control logic. As these components evolve, maintaining key quality attributes—such as modularity, testability, and architectural stability—becomes essential. Automated source-level refactoring offers a practical and systematic way to maintain software quality in dynamic CPS environments, where evolution occurs through ongoing development rather than autonomous runtime adaptation. Search-based refactoring methods identify optimal refactoring sequences to enhance software quality automatically. However, the multiplicity of quality attributes, the lack of formal definitions for them, and their non-correlation make it challenging to measure, reconcile, and appropriately apply quality attributes in search-based refactoring. This paper introduces an automated refactoring engine, CodART, which utilizes compiler principles to perform 18 different refactoring operations at the source code level, generating compilable code. Additionally, nine quality attributes are defined and evaluated to guide search-based refactoring effectively. The novel RNSGA-III algorithm is employed to better balance objectives in the nine-dimensional space. Many existing refactoring tools apply transformations at simplified code, UML, or AST level and do not directly output compilable, transformed source code. In contrast, CodART applies all transformations at the source level and produces compilable Java programs as output - a key requirement for integration into high-assurance CPS software pipelines. Compared to existing approaches, the proposed method enhances the number of quality attributes, refactorings, and optimization algorithms. The proposed algorithm improves software quality by an average of 9%, 12%, and 18% in large, medium, and small projects, respectively, surpassing state-of-the-art methods.