Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers

• URL: http://arxiv.org/abs/2603.16791v1
• Date: Tue, 17 Mar 2026 17:01:44 GMT
• Title: Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers
• Abstract summary: Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization.<n>We propose code as cognitive scaffolding, where cognitively guided automatically restructures code to improve clarity.<n>We operationalize this in CDDRefactorER, an automated approach grounded in Cognitive-Driven Development.
• Score: 2.824062086518866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization. While explanations may offer partial support, they typically do not restructure the code itself. We propose code refactoring as cognitive scaffolding, where cognitively guided refactoring automatically restructures code to improve clarity. We operationalize this in CDDRefactorER, an automated approach grounded in Cognitive-Driven Development that constrains transformations to reduce control-flow complexity while preserving behavior and structural similarity. We evaluate CDDRefactorER using two benchmark datasets (MBPP and APPS) against two models (gpt-5-nano and kimi-k2), and a controlled human-subject study with novice programmers. Across datasets and models, CDDRefactorER reduces refactoring failures by 54-71% and substantially lowers the likelihood of increased Cyclomatic and Cognitive complexity during refactoring, compared to unconstrained prompting. Results from the human study show consistent improvements in novice code comprehension, with function identification increasing by 31.3% and structural readability by 22.0%. The findings suggest that cognitively guided refactoring offers a practical and effective mechanism for enhancing novice code comprehension.

Related papers

• What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code [72.9921566968371]
  We revisit the claim that code improves reasoning through controlled pretraining experiments on a 10T-token corpus with fine-grained domain separation.<n>Code substantially improves programming ability but does not act as a general reasoning enhancer.<n>Cross-domain structured reasoning traces, such as code-text and math-text mixtures, are better explained.
  arXiv  Detail & Related papers  (2026-05-19T12:37:01Z)
• Do AI Agents Really Improve Code Readability? [0.0]
  Poor readability extends development time, increases bug-inducing risks, and contributes to technical debt.<n>With the rapid advancement of Large Language Models, AI agent-based approaches have emerged as a promising paradigm for automated decomposing complex tasks.<n>This study investigates the impact of AI agent-based specifically improves code readability.
  arXiv  Detail & Related papers  (2026-03-14T03:01:10Z)
• From Restructuring to Stabilization: A Large-Scale Experiment on Iterative Code Readability Refactoring with Large Language Models [5.31828955342405]
  Large language models (LLMs) are increasingly used for automated code tasks.<n>This article systematically study the capabilities of LLMs for code readability.
  arXiv  Detail & Related papers  (2026-02-25T12:05:25Z)
• From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability [46.83143241367452]
  Refactoring aims to improve code quality without altering program behavior.<n>Recent advances in Large Language Models (LLMs) have introduced new opportunities for automated code preservation.<n>We present an empirical study on LLM-driven classes using GPT-4o, applied to 100 Python classes from the ClassEval benchmark.<n>Our findings show that GPT-4o generally produces behavior-preservings that reduce code smells and improve quality metrics, albeit at the cost of decreased readability.
  arXiv  Detail & Related papers  (2026-01-19T15:22:37Z)
• Code Refactoring with LLM: A Comprehensive Evaluation With Few-Shot Settings [0.0]
  This study aims to develop a framework capable of performing accurate and efficient code across languages (C, C++, C#, Python, Java)<n>Java achieves the highest overall correctness up to 99.99% the 10-shot setting, records the highest average compilability of 94.78% compared to the original source code.
  arXiv  Detail & Related papers  (2025-11-26T14:47:52Z)
• A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code [49.09545217453401]
  Propensity Smelly Score (PSC) is a metric that estimates the likelihood of generating particular smell types.<n>We identify how generation strategy, model size, model architecture and prompt formulation shape the structural properties of generated code.<n> PSC helps developers interpret model behavior and assess code quality, providing evidence that smell propensity signals can support human judgement.
  arXiv  Detail & Related papers  (2025-11-19T19:18:28Z)
• Agentic Refactoring: An Empirical Study of AI Coding Agents [9.698067623031909]
  Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape.<n>These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks.<n>There is a critical lack of empirical understanding regarding how agentic is utilized in practice, how it compares to human-driven, and what impact it has on code quality.
  arXiv  Detail & Related papers  (2025-11-06T21:24:38Z)
• QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code [52.66657751895655]
  Large Language Models (LLMs) offer a compelling new paradigm: Neural Compilation.<n>This paper introduces NeuComBack, a novel benchmark dataset specifically designed for IR-to-assembly compilation.<n>We propose a self-evolving prompt optimization method that enables LLMs to evolve their internal prompt strategies.
  arXiv  Detail & Related papers  (2025-11-03T03:20:26Z)
• Refactoring with LLMs: Bridging Human Expertise and Machine Understanding [5.2993089947181735]
  We draw on Martin Fowler's guidelines to design instruction strategies for 61 well-known transformation types.<n>We evaluate these strategies on benchmark examples and real-world code snippets from GitHub projects.<n>While descriptive instructions are more interpretable to humans, our results show that rule-based instructions often lead to better performance in specific scenarios.
  arXiv  Detail & Related papers  (2025-10-04T19:40:42Z)
• Refactoring $\neq$ Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis [54.361900378970134]
  Just-in-time defect prediction (JIT-DP) aims to predict the likelihood of code changes resulting in software defects at an early stage.<n>Prior research has largely ignored code during both the evaluation and methodology phases, despite its prevalence.<n>We propose Code chAnge Tactics (CAT) analysis to categorize code and its propagation, which improves labeling accuracy in the JIT-Defects4J dataset by 13.7%.
  arXiv  Detail & Related papers  (2025-07-25T23:29:25Z)
• ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
  The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
  arXiv  Detail & Related papers  (2025-02-17T12:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.