mlsanityData to 7 Oct 2026

Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations

arXiv 2004.10777 ↗First posted 22 Apr 2020cs.SEcited by 18

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Abstract (arXiv)

In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 2; 2025: 5; 2026: 5.

18 papers.

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  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Taskscited by 24
  3. 2021The Prevalence of Code Smells in Machine Learning projectscited by 16
  4. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 15
  5. 2025Investigating The Smells of LLM Generated Codecited by 9
  6. 2022Code Smells for Machine Learning Applicationscited by 7
  7. 2025RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoringcited by 7
  8. 2022MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentationcited by 4
  9. 2025What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projectscited by 4
  10. 2022"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllintcited by 2
  11. 2023A systematic literature review on the code smells datasets and validation mechanismscited by 2
  12. 2024GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understandingcited by 2
6 more
  1. 2026Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metricscited by 2
  2. 2022Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrappingcited by 1
  3. 2025Automatic Identification of Machine Learning-Specific Code Smellscited by 0
  4. 2025Analysing Python Machine Learning Notebooks with Moosecited by 0
  5. 2026Learning Generalizable Multimodal Representations for Software Vulnerability Detectioncited by 0
  6. 2026REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoringcited by 0