Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 34 references
TL;DR
Design-AI, a novel framework that integrates deep evolutionary algorithms with adaptive intelligence to automate the identification and recommendation of refactoring opportunities, achieves high precision and recall in recommendations, outperforming state-of-the-art academic and commercial refactoring solutions.
Results show that ML-enhanced recommendations outperform traditional methods in accuracy, relevance, and impact on maintainability metrics, and highlight the potential of integrating ML into modern development practices to support developers in producing cleaner, more maintainable software systems.
Rohit Malhotra· International Journal of Mod...· 0 citations
A comprehensive taxonomy of data-centric refactoring strategies is introduced, their application across ML-driven software projects is investigated, and their impact on model accuracy, robustness, maintainability, and reproducibility is evaluated.
Fatou Diop· International Journal of Art...· 0 citations
The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed, but model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations
The engineering details of ADEPT are presented, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow.
Yidi Kao, Shawn Burnham, Tommi Rose Fahy et al.· 0 citations
Results indicate that static checks and test-guided, context-aware agentic repair can increase the reliability of LLM-generated refactorings, bringing them closer to practical integration within developer workflows.
Jonathan Cordeiro, Shayan Noei, Ying Zou· 1 citation
Large language models have shown promising capabilities in code refactoring, but existing approaches remain limited to method-level tasks. In this paper, we envision LLM-based refactoring as a continuous component of software maintenance rather than a tool invoked only for occasional manual refactoring. Under this vision, AI agents continuously monitor, evaluate, and improve codebases against explicit and evolving notions of software quality. We present a roadmap organized around five dimensions: the multi-objective optimization problem, quality definition and evaluation, multi-timescale integration of heterogeneous signals, architecture and design pattern, and trust in autonomous refactoring. We further identify integration into continuous delivery pipelines and cost considerations as cross-cutting concerns. For each dimension, we analyze the underlying challenges and pose open research questions. These dimensions define a research agenda for advancing autonomous refactoring from isolated code improvements to system-level quality maintenance.
Xin Sun, Daniel Ståhl, Kristian Sandahl et al.· 0 citations
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