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Published: 22 February 2023



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  • 基盤モデルをベースとするコード生成製品は、複雑で比較的長い提案が可能なため、開発者の生産性を大幅に向上させる

  • こうした製品は、周囲のコメントやコードを入力として用いることで、開発者の意図をより的確に理解できる。つまり、開発者とモデルがプログラミング「ペア」となって問題解決に取り組む。

  • トレーニングに用いるコードの来歴とライセンスに関するコンプライアンス、モデルのランタイム・パフォーマンス、生成されたコードの品質が重要な懸念事項であり、弱みとなる可能性もある。



  • 迅速に展開し開発者の生産性を最大限に高めることを目指して、機械学習 (ML) を活用したコーディング・アシスタントを試験運用する。

  • 数人のソフトウェア・エンジニアを厳選し、自然言語とコーディング・プラクティスを組み合わせて、最小限の労力でコード生成を最適化する方法を解明する「プロンプト・エンジニアリング」という新しいスキルを習得させる。

  • 著作権侵害、コード品質、パフォーマンスの低いコード生成といった新たな問題を特定・監視・解決するための部門横断型チームを形成し、このチームを先導する。

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Harutoshi Katayama

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