Can We Trust the AI Pair Programmer? Copilot for API Misuse Detection and Correction

2025-09-20

AI Summary

API misuse frequently induces security vulnerabilities and system failures; however, existing static analysis or machine learning–based detection tools operate post-development, limiting timely intervention. This paper presents the first systematic empirical evaluation of GitHub Copilot’s capability to detect and rectify API misuse in real time within the IDE environment. We construct a benchmark of 740 misuse instances and 147 correct usage cases derived from MUBench, encompassing both human-authored and AI-generated scenarios, and conduct experiments in VS Code. Results show Copilot achieves 86.2% detection accuracy, with 91.2% precision and 92.4% recall, and successfully repairs over 95% of identified misuses. The study reveals strong performance on common, syntactic misuses but identifies limitations in handling composite logic and context-sensitive patterns. Our findings demonstrate the feasibility and promise of AI-powered pair programming tools for proactive, development-stage prevention of API-related defects.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

API misuse introduces security vulnerabilities, system failures, and increases maintenance costs, all of which remain critical challenges in software development. Existing detection approaches rely on static analysis or machine learning-based tools that operate post-development, which delays defect resolution. Delayed defect resolution can significantly increase the cost and complexity of maintenance and negatively impact software reliability and user trust. AI-powered code assistants, such as GitHub Copilot, offer the potential for real-time API misuse detection within development environments. This study evaluates GitHub Copilot's effectiveness in identifying and correcting API misuse using MUBench, which provides a curated benchmark of misuse cases. We construct 740 misuse examples, manually and via AI-assisted variants, using correct usage patterns and misuse specifications. These examples and 147 correct usage cases are analyzed using Copilot integrated in Visual Studio Code. Copilot achieved a detection accuracy of 86.2%, precision of 91.2%, and recall of 92.4%. It performed strongly on common misuse types (e.g., missing-call, null-check) but struggled with compound or context-sensitive cases. Notably, Copilot successfully fixed over 95% of the misuses it identified. These findings highlight both the strengths and limitations of AI-driven coding assistants, positioning Copilot as a promising tool for real-time pair programming and detecting and fixing API misuses during software development.

Problem

• Detecting API misuse that causes security vulnerabilities and system failures
• Evaluating AI code assistants for real-time API misuse detection during development
• Assessing Copilot's ability to identify and correct API misuse patterns

Innovation

• Evaluated Copilot using MUBench for API misuse detection
• Achieved high accuracy and recall in identifying common misuses
• Successfully fixed over 95% of detected API misuses