Can We Trust the AI Pair Programmer? Copilot for API Misuse Detection and Correction
Bibliographic record
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$\mathbf{9 1. 2 \%}$, and recall of$\mathbf{9 2. 4 \%}$. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".