人体によって書かれた,人工知能によって生成されたコードのソース分類のためのデータセット
Ghizlane Boukili1, Said El Garouani1, Jamal Riffi1
1LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, 30003, Morocco.
Data in brief
|February 18, 2026
まとめ
10,000個のコードサンプルからなる新しいデータセットは,AIによって生成されたコードを検出するのに役立ちます. このリソースは,コンピュータサイエンスの教育者が,人間と人工知能のプログラミングを区別し,学術的な整合性を改善するのに役立ちます.
科学分野:
- コンピュータサイエンス コンピュータサイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
背景:
- AIのコード生成ツールは,コンピュータサイエンスの教育における学生の信頼性を検証する上で課題を提起しています.
- 既存の汎用AI検出ツールは,プログラミング言語の特殊性により,AIによって生成されたコードを正確に識別するには不十分です.
研究 の 目的:
- ドメイン特有のAIコード検出ツールを開発するための専門データセットを導入する.
- AIで生成されたコード検出の研究のためのリソースのギャップを埋めること.
主な方法:
- 10,000個の注釈されたコードサンプル (5000は人間によって書かれ,5000はAIによって生成された) を含むデータセットの作成.
- Python,Java,C,C++のサンプルを含める
- ChatGPT API で生成されたAIで生成されたサンプル; 公共のリポジトリから調達されたヒトサンプル.
- 各サンプルには原産地 (人間またはAI) によるラベルを貼り,モデルトレーニングを行う.
主要な成果:
- このデータセットは,コードのソース差別のための機械学習とディープラーニングモデルの堅牢なトレーニングを可能にします.
- AIによって生成されたコードを検出するための特殊なツールの開発を容易にする.
結論:
- この特殊なデータセットは,AIによって生成されたコードの検出に関する研究を進めるために極めて重要です.
- データセットと実験コードの公開は,さらなる学術的調査とツール開発をサポートします.
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