[{"data":1,"prerenderedAt":705},["ShallowReactive",2],{"/ja-jp/blog/quickly-resolve-broken-ci-cd-pipelines-with-ai/":3,"navigation-ja-jp":38,"banner-ja-jp":454,"footer-ja-jp":467,"Itzik Gan Baruch":677,"next-steps-ja-jp":690},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":28,"_id":31,"_type":32,"title":33,"_source":34,"_file":35,"_stem":36,"_extension":37},"/ja-jp/blog/quickly-resolve-broken-ci-cd-pipelines-with-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},"破損したCI/CDパイプラインをAIで迅速に解決","CI/CDパイプラインの失敗が発生すると、遅延、生産性の低下、ストレスが生じます。AI搭載の根本原因分析なら、より迅速でスマートな問題解決ができます。","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097355/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750097355230.png","https://about.gitlab.com/blog/quickly-resolve-broken-ci-cd-pipelines-with-ai","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"破損したCI/CDパイプラインをAIで迅速に解決\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Itzik Gan Baruch\"}],\n        \"datePublished\": \"2024-12-03\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22,"updatedDate":27},[18],"Itzik Gan Baruch","2024-12-03","CI/CDパイプラインは、ソフトウェア開発における効率の要です。これにより、チームはコードのテスト、ビルド、デプロイを迅速に行うことができます。しかし、こうしたパイプラインが破損すると、すべてに遅れが生じ、締め切りに間に合わなくなります。また、デベロッパーは、問題の解決に取り組み、プロジェクトを軌道に戻そうと奮闘する中で、不満を抱えたままになってしまいます。\n\n![失敗したジョブを複数含むCI/CDパイプライン](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097362/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097362772.png)\n\n\u003Ccenter>\u003Ci>失敗したジョブを複数含むCI/CDパイプライン\u003C/i>\u003C/center>\u003Cbr>\u003C/br>\n\n**では、そもそもなぜパイプラインは破損してしまうのでしょうか？** 詳しく見ていきましょう。\n\n## パイプラインの失敗が生じる理由\n\nパイプラインの失敗は、[CI/CDパイプライン](https://about.gitlab.com/ja-jp/topics/ci-cd/cicd-pipeline/)内の自動化されたワークフロー（ビルド、テスト、コードのデプロイなどを含む一連のステップ）が正常に実行されず、エラーメッセージで終了する場合に発生します。この失敗により、コードが適切にビルド、テスト、またはデプロイされないことになり、ソフトウェアデリバリーが遅れるだけでなく、トラブルシューティングが必要になります。\n\nパイプラインの失敗にはさまざまな原因があります。一般的な原因には次のようなものがあります。\n\n- 構文エラー：セミコロンの不足や変数名の間違いなど、コード内の小さなミス\n- テストの失敗：コードの不具合、誤設定、依存関係の不一致により、ユニットテストや統合テストが失敗する\n- 設定ミス：パイプライン設定や環境設定により、ビルドやデプロイが失敗する\n\nさらに複雑な問題もあります。\n\n- Infrastructure-as-Code（[IaC](https://about.gitlab.com/ja-jp/topics/gitops/infrastructure-as-code/)）の問題：TerraformスクリプトやCloudFormationテンプレートのエラーなど、クラウドインフラストラクチャのプロビジョニングで問題が発生し、正常なデプロイができない\n- KubernetesとGitOpsの課題：[Kubernetesクラスター](https://about.gitlab.com/blog/kubernetes-the-container-orchestration-solution/)の設定ミスや[GitOps](https://about.gitlab.com/ja-jp/topics/gitops/)ワークフローの問題（Kubernetesの状態とGitリポジトリの同期など）により、原因特定が困難なパイプラインの失敗が発生する\n-  長くて複雑なスタックトレース：システムの深い部分でエラーが発生すると、特に複数のコンポーネントやサービスにまたがるとスタックトレースが長く複雑になり、解読が難しくなる\n\nこうした理由から、トラブルシューティングがより難しく時間がかかるものになります。根本原因を特定するには、複雑なログの確認や設定ファイルの見直し、さまざまな解決策の検証が必要になるからです。\n\n## 失敗したパイプラインの実際の影響\n\nパイプラインが失敗すると、デプロイが遅れるだけでなく、ストレスや不満も引き起こします。デベロッパーは作業を中断し、トラブルシューティングに専念する必要があり、多くの場合、チームやプロジェクト全体、他のタスクへと影響が広がります。 その結果、納期を守るのがさらに難しくなり、チーム全体のプレッシャーが増えます。しかし、なぜ手動でのトラブルシューティングはこれほど大きなストレスになるのでしょうか？\n\n### 手動でのトラブルシューティング\n\n壊れたパイプラインを修正するのにかかる時間はケースによってさまざまです。次のような要素に依存します：\n\n- デベロッパーがプロジェクトをどれだけ理解しているか  \n- 類似の問題に対する経験  \n- 全体的な問題解決能力\n\nログを手動で調べて原因を特定するのは、骨の折れる単調な作業です。ログは、アプリケーションエラーやシステムメッセージを含むあらゆるところに存在し、多くの場合、整理されておらず解釈が難しいことがあります。その上、パイプラインの修正には、通常、タスク間を行ったり来たりしながら作業を進める必要があり、さらに時間がかかります。\n\nそこで役に立つのが[GitLab Duo](https://about.gitlab.com/ja-jp/gitlab-duo/)です。GitLab Duoは、そういった複雑なデータをすべて分析し、問題を素早く見つけ出します。専門知識がなくても何が原因かを特定できるようプロセスをシンプルにします。AIを活用することで、パイプラインの修正がより速く、簡単で、はるかにストレスの少ないものになります。\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176104/Blog/zxvvu7p9vc3qpmwl32ya.png\" alt=\"broken pipeline\">\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176108/Blog/bpx6dqilfhltzboyp8k8.png\" alt=\"fix suggestions for broken pipelines\">\n\n## 生成AIによるGitLab Duo根本原因分析\n\n[GitLab Duo根本原因分析（RCA）](https://docs.gitlab.com/ee/user/gitlab_duo/#root-cause-analysis)を活用すれば、CI/CDパイプラインが破損しても、何時間もかけて手動でトラブルシューティングする必要はありません。このAI搭載ツールは、失敗の原因を正確かつ素早く特定し、修正案をDevSecOpsプラットフォーム内で提供します。スタックトレースがどれだけ長く複雑でも、RCAはすべてのデータを分析し、分かりやすく処理して、実行可能なインサイトを提供します。\n\nエラーの原因の正確な情報を提供してくれるだけでなく、修正手順を示し、注意が必要なファイルやコード行まで具体的に教えてくれます。 さらに、コード提案で全体を正常に戻すサポートをするため、トラブルシューティングが非常に速く、シンプルに進みます。\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176111/Blog/nmagby9hoksskogve53m.png\" alt=\"root cause of failure\">\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176115/Blog/dndis1cedwbmbnj33q3v.png\" alt=\"example fix\">\n\n## フォローアップ質問で会話を掘り下げる\n\nGitLab Duo RCAでは、答えを得た後にさらに詳細な質問をして、問題を掘り下げることができます。代替案を検討したい場合も、リポジトリ内の他のファイルやイシュー、エピックを参照にすることで、[コンテキスト](https://docs.gitlab.com/ee/user/gitlab_duo_chat/index.html#the-context-chat-is-aware-of)を追加できます。たとえば、`.gitlab-ci.yml`ファイルをIDEで開き、「このファイルとCI/CDパイプラインの分析結果をもとに、どのようにパイプラインを改善するのが最適だと思いますか？」とChatで質問できます。\n\n## プライバシー第一で、すべてGitLab内で処理\n\nGitLab Duo RCAの最大の利点のひとつは、GitLab内でそのまま利用できることです。ツールを切り替えたり、外部のサポートを探したりする必要はありません。また、[ログや機密データを外部のAIソリューションに送信する必要もありません。GitLab内で安全に処理](https://about.gitlab.com/ja-jp/privacy/)されます。RCAはGitLabにシームレスに統合されており、プライバシーを守りながら有益なインサイトを提供します。\n\n![パイプラインの破損 - 画像6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097363/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097362773.png)\n\n## 今すぐ始める\n\nAIを活用して開発プロセスをパワーアップし、よりスムーズかつスピーディーに進めてみませんか？以下のGitLab Duo Enterprise製品ツアーで、プランニング、コーディング、トラブルシューティング、デプロイまで、GitLab DuoのAI搭載機能が全工程にわたって与える効果をご覧ください。ツアーを開始するには、下の画像をクリックしてください。\n\n[![GitLab Duo Enterpriseツアー](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097363/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-12-02_at_12.41.10_PM_aHR0cHM6_1750097362774.png)](https://gitlab.navattic.com/duo-enterprise)\n\n> [GitLab Duoの無料トライアルを今すぐお試しいただけます。](https://about.gitlab.com/ja-jp/solutions/gitlab-duo-pro/sales/)\n\n\u003Cbr>\n\u003Cbr>\n\n*監修：ソリス ジェレズ / Jerez Solis [@jerezs](https://gitlab.com/jerezs)\u003Cbr>\n（GitLab合同会社 ソリューションアーキテクト本部 ソリューションアーキテクト）*\n","ai-ml",[23,24,25,26],"AI/ML","DevSecOps platform","tutorial","features","2025-03-12",{"slug":29,"featured":6,"template":30},"quickly-resolve-broken-ci-cd-pipelines-with-ai","BlogPost","content:ja-jp:blog:quickly-resolve-broken-ci-cd-pipelines-with-ai.yml","yaml","Quickly Resolve Broken Ci Cd Pipelines With 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