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Comment Traps: How Defective Commented-out Code Augment Defects in AI-Assisted Code Generation

Yuan Huang, Yukang Zhou, Xiangping Chen, Zibin Zheng

TL;DR

The experimental results show that defective CO code in the context causes AI coding assistants to generate more defective code, reaching up to 58.17 percent, and underscore the need for improved robustness and security measures in AI coding assistants.

Abstract

With the rapid development of large language models in code generation, AI-powered editors such as GitHub Copilot and Cursor are revolutionizing software development practices. At the same time, studies have identified potential defects in the generated code. Previous research has predominantly examined how code context influences the generation of defective code, often overlooking the impact of defects within commented-out code (CO code). AI coding assistants' interpretation of CO code in prompts affects the code they generate. This study evaluates how AI coding assistants, GitHub Copilot and Cursor, are influenced by defective CO code. The experimental results show that defective CO code in the context causes AI coding assistants to generate more defective code, reaching up to 58.17 percent. Our findings further demonstrate that the tools do not simply copy the defective code from the context. Instead, they actively reason to complete incomplete defect patterns and continue to produce defective code despite distractions such as incorrect indentation or tags. Even with explicit instructions to ignore the defective CO code, the reduction in defects does not exceed 21.84 percent. These findings underscore the need for improved robustness and security measures in AI coding assistants.

Comment Traps: How Defective Commented-out Code Augment Defects in AI-Assisted Code Generation

TL;DR

The experimental results show that defective CO code in the context causes AI coding assistants to generate more defective code, reaching up to 58.17 percent, and underscore the need for improved robustness and security measures in AI coding assistants.

Abstract

With the rapid development of large language models in code generation, AI-powered editors such as GitHub Copilot and Cursor are revolutionizing software development practices. At the same time, studies have identified potential defects in the generated code. Previous research has predominantly examined how code context influences the generation of defective code, often overlooking the impact of defects within commented-out code (CO code). AI coding assistants' interpretation of CO code in prompts affects the code they generate. This study evaluates how AI coding assistants, GitHub Copilot and Cursor, are influenced by defective CO code. The experimental results show that defective CO code in the context causes AI coding assistants to generate more defective code, reaching up to 58.17 percent. Our findings further demonstrate that the tools do not simply copy the defective code from the context. Instead, they actively reason to complete incomplete defect patterns and continue to produce defective code despite distractions such as incorrect indentation or tags. Even with explicit instructions to ignore the defective CO code, the reduction in defects does not exceed 21.84 percent. These findings underscore the need for improved robustness and security measures in AI coding assistants.
Paper Structure (33 sections, 1 equation, 9 figures, 6 tables, 1 algorithm)

This paper contains 33 sections, 1 equation, 9 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. An Empirical Study: The Prevalence of CO Code
  3. GitHub Repository Collection
  4. Prevalence of CO Code in the GitHub Repositories
  5. Prevalence of Defective CO Code
  6. Related Work
  7. AI-Assisted Code Generation
  8. Factors Influencing the Performance of AI-Generated Code
  9. Methodology
  10. Dataset Preparation
  11. Prompt Construction
  12. Code Generation
  13. Defects Detection
  14. Experiments
  15. (RQ1) Impact of CO code on defect rates in AI-generated code
  16. ...and 18 more sections

Figures (9)

  • Figure 1: A Motivating Example on the Impact of Defective CO Code on AI Coding Assistants
  • Figure 2: Research methodology
  • Figure 3: Schematic illustration of dataset construction: (a) the original Python file, (b) the code context, and (c) the defective CO code.
  • Figure 4: Illustration of the constructed prompt: demonstrating the placement of defective CO code one line above the completion point
  • Figure 5: Comparison of the number of defects generated by the RandomInsertion and FullInsertion groups in GitHub Copilot
  • ...and 4 more figures