Bibliographic record
Abstract
GitHub and OpenAI recently launched Copilot, an "AI pair programmer" that utilizes the power of Natural Language Processing, Static Analysis, Code Synthesis, and Artificial Intelligence. Given a natural language description of the target functionality, Copilot can generate corresponding code in several programming languages. In this paper, we perform an empirical study to evaluate the correctness and understandability of Copilot's suggested code. We use 33 LeetCode questions to create queries for Copilot in four different programming languages. We evaluate the correctness of the corresponding 132 Copilot solutions by running LeetCode's provided tests, and evaluate understandability using SonarQube's cyclomatic complexity and cognitive complexity metrics. We find that Copilot's Java suggestions have the highest correctness score (57%) while JavaScript is the lowest (27%). Overall, Copilot's suggestions have low complexity with no notable differences between the programming languages. We also find some potential Copilot shortcomings, such as generating code that can be further simplified and code that relies on undefined helper methods.
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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 | 0.009 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".