[{"data":1,"prerenderedAt":709},["ShallowReactive",2],{"/ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai":3,"navigation-ja-jp":38,"banner-ja-jp":452,"footer-ja-jp":465,"footer-source-/ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/":675,"Haim Snir":681,"next-steps-ja-jp":694},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":27,"_id":31,"_type":32,"title":33,"_source":34,"_file":35,"_stem":36,"_extension":37},"/ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"GitLab Duo開発の現場から：AIインパクト分析ダッシュボードによるAIのROI測定","このブログシリーズでは、「コード提案利用率」のような、詳しいメトリクスを表示する新機能を継続的に取り上げ、AI投資の効果について理解を深めていただくことが狙いです。","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098611/Blog/Hero%20Images/Blog/Hero%20Images/blog-hero-banner-1-0178-820x470-fy25_7JlF3WlEkswGQbcTe8DOTB_1750098611370.png","https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"GitLab Duo開発の現場から：AIインパクト分析ダッシュボードによるAIのROI測定\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Haim Snir\"}],\n        \"datePublished\": \"2024-05-15\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22,"updatedDate":26},[18],"Haim Snir","2024-05-15","***生成系AIは、ソフトウェアの開発、セキュリティ保護、運用のプロセスを簡素化し、ソフトウェア開発業界に大きな変革をもたらしています。GitLabの製品チームとエンジニアリングチームが手掛ける新しいブログシリーズでは、企業全体に統合すべきAI機能をどのように作成、テスト、そしてデプロイするか明らかにし、DevSecOpsチームがよりよいソフトウェアを顧客に届ける上で、GitLab Duoの新機能がどのように役立つのかご理解いただける内容になっています。***\n\n組織が[GitLab Duo](https://about.gitlab.com/gitlab-duo/)（DevSecOpsワークフローを最適化する各種AI機能）を導入するにあたり、ビジネスリーダーやエンジニアリングリーダーは、こうしたテクノロジーのROI（投資対効果）をリアルタイムで可視化する必要があります。ソフトウェア開発におけるAIの有効性を評価するには、詳細な使用データ、パフォーマンスの改善、その他の[生産性メトリクス](https://about.gitlab.com/blog/measuring-ai-effectiveness-beyond-developer-productivity-metrics/)に加え、スピード、セキュリティ、品質のバランスをとることが必要です。GitLabはこれに対処するために、AIのROIを測定する新たな手段としてGitLab DuoのAIインパクト分析ダッシュボードを導入しました。この機能はGitLab 17.0から利用可能です。\n\n> ライブデモイベント開催決定！GitLab 17バーチャルローンチイベントで、AI主導のソフトウェア開発の未来を発見してみませんか（[今すぐ登録する](https://about.gitlab.com/eighteen/)）。\n\n## GitLab DuoのAI搭載機能のROI\n\nソフトウェア開発ライフサイクルにおけるAIの影響を正確に評価するため、企業は次のような機能をリクエストしています：\n- AIへの投資によって向上したメトリクスの可視化\n- AIを使用しているチームと使用していないチームのパフォーマンスの比較\n- AI導入の進捗の追跡\n- 大量のパフォーマンスデータからのインサイトの自動抽出\n\nGitLab DuoのAIインパクト分析ダッシュボードはこのようなさまざまな機能に加え、カスタマイズ可能な可視化機能も備えており、活用することで、以下が可能になります。\n\n- **AI導入率のモニタリング**：AIの導入率を観察することで、組織はテクノロジー投資のROIを最大化するための戦略を評価できます。\n- **パフォーマンス改善の追跡**：リーダーはパフォーマンスメトリクスを追跡し、AI導入後の変化を観察することで、AI機能がもらたすメリットとビジネス価値を迅速に評価できます。\n\n## AIインパクト分析ダッシュボードとは？\n\nAIインパクト分析ダッシュボードの初回リリースでは、GitLab Duoのコード提案の導入に関する、以下のインサイトとメトリクスの提供に焦点が当てられています。\n\n- **詳細な使用状況メトリクス**：コード提案の月間利用率とユニークコードコントリビューターの総数を比較します。これをもとに、チーム内でコード提案をどの程度活用できているかを把握できます。\n- **相関性のモニタリング**：プロジェクトやグループ内におけるAI使用率の動向が、主要な生産性メトリクスにどのような影響を与えるかを、当月と過去6か月間のデータに基づき表示します。\n  - この相関分析に関連し、独立変数（原因）として「コード提案利用率」という新しいメトリクスが導入されました。コード提案の月間利用率は、コード提案の月間ユニークユーザー数を月間ユニーク[コントリビューター](https://docs.gitlab.com/ee/user/profile/contributions_calendar.html#user-contribution-events)総数で割ることで算出されます。GitLabでは、月間コードコントリビューター総数を基準とし、プッシュ済みイベントの記録があるユーザーのみをこの計算に含めています。\n  - 依存変数（効果）として、「サイクルタイム」「リードタイム」「デプロイ頻度」という[パフォーマンスメトリクス](https://docs.gitlab.com/ee/user/analytics/value_streams_dashboard.html#dashboard-metrics-and-drill-down-reports)と、「変更失敗率」と「致命的な脆弱性」という[品質とセキュリティのメトリクス](https://docs.gitlab.com/ee/user/analytics/value_streams_dashboard.html#dashboard-metrics-and-drill-down-reports)が追加されました。\n- **比較ビュー**：AIを使用しているチームとそうでないチームのパフォーマンスを比較し、スピード、品質、セキュリティ脆弱性のバランスを管理します。\n\n![AI利用率とSDLCパフォーマンスの比較](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098621/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750098620998.png)\n\n## AIインパクト分析ダッシュボードに今後導入される機能は？\n\nGitLabでは、AIインパクト分析ダッシュボードの更なる機能強化に向けて計画を進めています。以下に、その一部をご紹介します。\n\n1.「GitLab Duoシート：アサイン済みと使用済み」「コード提案：採用率（%）」「GitLab Duoチャット：ユニークユーザー数」といった新しいタイルデータを導入することから得られる可視性により、GitLab Duoの使用パターンをより深く理解できるようになります。\n\n![AI Impactダッシュボード - image 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098621/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-07-17_at_12.50.31_aHR0cHM6_1750098620999.png)\n\n2. 比較棒グラフを新たに導入し、あるメトリクスの変化が他のメトリクスの変化とどのように関連しているかを観察できるようになります。\n\n![AIインパクト比較棒グラフ](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098621/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750098621000.png)\n\n3. [コントリビュート分析レポート](https://docs.gitlab.com/ee/user/group/contribution_analytics/index.html)のAI統計の導入により、ユーザーがAI機能をどのように利用しているのかを把握できます。 以下のように、どのユーザーがAI機能を活用しているのか、またそのパフォーマンスの推移が表示されます。\n![コントリビュート分析レポート](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098621/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098621001.png)\n\n## 始めてみましょう\n\nAIインパクト分析ダッシュボードは、AIによる実際のビジネス成果を実証するだけでなく、DevSecOpsライフサイクルにおける将来のAI最適化に関して、より多くの情報に基づいた意思決定を促進する可能性があります。今後の機能については、[AIインパクト分析ダッシュボードのエピックをご覧ください](https://gitlab.com/groups/gitlab-org/-/epics/12978)。また、こちらからフィードバックやご質問もぜひお寄せください。\n\n[今すぐ、GitLab Duoの無料トライアルとAIインパクト分析ダッシュボード](https://about.gitlab.com/gitlab-duo/#free-trial)をお試しください。\n\n## 「GitLab Duo開発の現場から」シリーズをもっと読む\n\n- [GitLab Duo開発の現場から：AIモデルの大規模な検証とテスト方法](https://about.gitlab.com/ja-jp/blog/developing-gitlab-duo-how-we-validate-and-test-ai-models-at-scale/)\n- [GitLab Duo開発の現場から：AIインパクト分析ダッシュボードによるAIのROI測定](https://about.gitlab.com/ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/)\n- [GitLab Duo開発の現場から：GitLabにおけるAI機能のドッグフーディングの取り組み](https://about.gitlab.com/ja-jp/blog/developing-gitlab-duo-how-we-are-dogfooding-our-ai-features/)\n- [GitLab Duo開発の現場から：AI生成コードの安全性確認と詳細なテスト](https://about.gitlab.com/ja-jp/blog/how-gitlab-duo-helps-secure-and-thoroughly-test-ai-generated-code/)\n\n_免責事項：このブログには、今後の製品、機能、機能に関する情報が含まれています。このブログ投稿の情報は、情報提供のみを目的としている点にご留意ください。これらの情報は、購入や計画の際の判断材料として使用すべきものではありません。すべてのプロジェクトと同様に、このブログおよびリンク先のページに記載されている項目は、変更または遅延される場合があります。製品、機能、機能の開発、リリース、タイミングは、GitLab Inc.の独自の裁量に委ねられます。_\n\n*\u003Cbr>\n監修：\u003Cbr>\n*監修：大井 雄介 [@yoi_gl](https://gitlab.com/yoi_gl)\n（GitLab合同会社 ソリューションアーキテクト本部 本部長）*\n\n*監修：小松原 つかさ [@tkomatsubara](https://gitlab.com/tkomatsubara)\u003Cbr>\n（GitLab合同会社 ソリューションアーキテクト本部 シニアパートナーソリューションアーキテクト）*\n","ai-ml",[23,24,25],"AI/ML","performance","features","2024-06-13",{"slug":28,"featured":29,"template":30},"developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai",true,"BlogPost","content:ja-jp:blog:developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai.yml","yaml","Developing Gitlab Duo Ai Impact Analytics Dashboard Measures The Roi Of Ai","content","ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai.yml","ja-jp/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai","yml",{"_path":39,"_dir":40,"_draft":6,"_partial":6,"_locale":7,"data":41,"_id":448,"_type":32,"title":449,"_source":34,"_file":450,"_stem":451,"_extension":37},"/shared/ja-jp/main-navigation","ja-jp",{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":392,"minimal":426,"duo":439},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/ja-jp/","gitlab 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