● Covered by 1 source · 1 reportLow impact1 neutral
New AI code comment detector rebuilt with public data and improved foundation
Key points
- Classifier rebuilt using public data for shareability.
- Achieves 77% balanced accuracy in classifying AI vs. human comments.
- Provides calibrated predictive percentages for verdict reliability.
- Allows inspection of feature activations for classification transparency.
Rebuilt Classifier for Public Use
A new AI code comment classifier has been developed, addressing previous limitations by being built entirely on public data. This allows for the sharing of code and data, which was not possible with an earlier version that utilized private datasets. The classifier is accessible via a web interface where users can test it without their input leaving the browser.
Performance Metrics
The classifier demonstrates a balanced accuracy of 77%, indicating its effectiveness in correctly identifying human-written versus AI-generated comments, assuming an equal distribution of both. It also provides a calibrated predicted percentage, which can be interpreted as the probability that a specific verdict is correct. This calibration is validated through a calibration curve, showing close alignment with reference probabilities.
Transparency and Error Analysis
To enhance transparency, the classifier's user interface allows users to click on any part of the classified text to view which features contributed to the overall judgment. Analysis of its confusion matrix reveals that the classifier correctly identifies human input 73% of the time and AI-generated input 80% of the time. While the aggregate mistake rate is around 25%, the calibrated predictive percentage for individual judgments helps users assess the risk of false positives or negatives.
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Reporting from
A new AI code comment classifier has been developed using public data, offering a balanced accuracy of 77% in distinguishing human-written from AI-generated comments. This tool provides calibrated probability percentages for its verdicts and allows users to inspect feature activations for individual classifications, addressing limitations of a previous version built on private data.