2020· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
The study highlights the potential of intelligent, data-driven approaches to enhance software quality and support continuous code improvement by leveraging code metrics, syntactic and semantic features, and historical refactoring data.
Abstract
Code smells are indicative of poor software design, maintainability issues, or potential defects, and their early detection is critical for high-quality software development. Traditional detection methods rely heavily on manual inspection or rule-based static analysis, which are often time-consuming, error-prone, and limited in adaptability. This paper explores the use of machine learning techniques for automated code smell detection and resolution. By leveraging code metrics, syntactic and semantic features, and historical refactoring data, ML models can identify patterns associated with common code smells and recommend targeted resolution strategies. The proposed approach is evaluated on open-source and industrial software projects, demonstrating improvements in detection accuracy, maintainability, and developer productivity. The study highlights the potential of intelligent, data-driven approaches to enhance software quality and support continuous code improvement.
This study investigates the application of UniXcoder, a pre-trained transformer model for source code, to classify Java source code methods across multiple projects as smell or clean, with a particular focus on the Switch Statements smell, and confirms that pre-trained transformer models, particularly UniXcoder, are ca...
Hanson Prihantoro Putro, Umi Laili Yuhana, E. M. Yuniarno et al.· IEEE Access· 0 citations
SmellCC, a Visual Studio Code extension that augments SonarQube with an LLM-based pipeline to automatically detect and refactor Python code smells, provides in-place, one-click remediation for the top-10 most frequent smells, effectively preventing the accumulation of technical debt during development.
Xiaoting Zhang, Yujie Zhang, Zhi-Peng Gao et al.· 0 citations
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
This paper focuses on four common smells and considers five prompts of diverse complexity, asking the model for detecting and removing the identified code smells, and suggests that general-purpose LLMs cannot be reliably used for that.
Giorgia Paisi, Francesca Arcelli Fontana, Bartosz Walter· WiPiEC Journal - Works in Pr...· 0 citations
This survey synthesises research from 2020 to 2026 on AI-driven techniques for code review and software quality evaluation, focusing on two complementary paradigms: Automated Machine Learning (AutoML) and Large Language Models (LLMs).
R. Borgalli, Uma Raj, Shwetha Shetty· International Conference on...· 0 citations
Recently, Developers have been relying on AI tools to support them in their daily work by generating code. While the use of large language model-based AI tools has improved productivity, the quality of the generated code wasn't always optimal. In a lot of cases, the code includes design issues known as code smells, whi...
Y. Younes, Yousef Elsheikh· IEEE Jordan Conference on Ap...· 0 citations
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