[{"data":1,"prerenderedAt":710},["ShallowReactive",2],{"/fr-fr/blog/quickly-resolve-broken-ci-cd-pipelines-with-ai/":3,"navigation-fr-fr":38,"banner-fr-fr":458,"footer-fr-fr":470,"Itzik Gan Baruch":682,"next-steps-fr-fr":695},{"_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},"/fr-fr/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},"Échecs de pipelines CI/CD : comment les corriger rapidement avec l'IA","Découvrez comment l'analyse des causes profondes alimentée par l'IA résout les problèmes d'exécution de pipelines CI/CD de façon plus intelligente et plus rapidement.","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\": \"Échecs de pipelines CI/CD : comment les corriger rapidement avec l'IA\",\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","Les pipelines CI/CD sont essentiels à l'efficacité dans le développement logiciel, car ils aident les équipes à tester, compiler et déployer le code rapidement. Cependant, lorsque ces pipelines échouent, c'est tout le cycle de développement qui se trouve ralenti : les délais ne sont pas respectés et les équipes doivent corriger le code tout en faisant avancer les projets en cours.\n\n![Pipeline CI/CD avec plusieurs jobs en échec](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097362/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097362772.png)\n\n\u003Ccenter>\u003Ci>Pipeline CI/CD avec plusieurs jobs en échec\u003C/i>\u003C/center>\u003Cbr>\u003C/br>\n\n**Mais quelles sont les raisons pour lesquelles les pipelines ne s'exécutent-ils pas ?** Analysons cela ensemble.\n\n## Pourquoi les pipelines CI/CD échouent-ils ? \n\nOn parle d'échec lorsque le workflow automatisé de votre [pipeline CI/CD](https://about.gitlab.com/fr-fr/topics/ci-cd/cicd-pipeline/), comprenant généralement des étapes telles que la compilation, les tests et le déploiement du code, ne s'exécute pas comme prévu et génère un message d'erreur. Par conséquent, le code n'est pas correctement compilé, testé ou déployé, ce qui retarde la livraison du logiciel et nécessite d'identifier et de résoudre le problème rencontré. \n\nLes échecs de pipelines CI/CD peuvent survenir pour diverses raisons. Voici les causes les plus fréquentes :\n- __Erreurs de syntaxe :__ une petite erreur dans le code, comme un point-virgule manquant ou un nom de variable incorrect, peut faire échouer le pipeline.\n- __Échec des tests :__ les tests unitaires ou d'intégration peuvent échouer en raison d'un code défectueux, de configurations incorrectes ou de dépendances incompatibles.\n- __Configurations erronées :__ des paramètres incorrects du pipeline ou des configurations d'environnement peuvent provoquer l'échec des compilations ou des déploiements.\n\nD'autres problèmes plus complexes peuvent également s'ajouter comme :\n- Des problèmes d'Infrastructure as Code ([IaC](https://about.gitlab.com/fr-fr/topics/gitops/infrastructure-as-code/)) : des erreurs lors du provisionnement de l'infrastructure cloud, telles que des fautes dans les scripts Terraform ou les templates CloudFormation, peuvent empêcher le déploiement.\n- Les défis que posent Kubernetes et GitOps : des configurations erronées dans les [clusters Kubernetes](https://about.gitlab.com/fr-fr/blog/kubernetes-the-container-orchestration-solution/) ou des problèmes affectant les workflows [GitOps](https://about.gitlab.com/fr-fr/topics/gitops/) (par exemple, la synchronisation de l'état des ressources Kubernetes avec les dépôts [Git](https://about.gitlab.com/fr-fr/blog/what-is-git/ \"Qu'est-ce que Git ? \")) peuvent causer l'échec des pipelines et rendre difficile le diagnostic.\n- Des traces de piles d'appels longues et désordonnées : lorsqu'une erreur se produit dans les profondeurs du système, les traces de piles d'appels peuvent devenir longues et difficiles à déchiffrer, en particulier lorsqu'elles couvrent plusieurs composants ou services.\n\nCes obstacles compliquent et ralentissent le dépannage, car les équipes de développement doivent souvent passer au crible des logs détaillés et complexes, examiner les fichiers de configuration et tester différentes solutions afin d'identifier la cause profonde.\n\n## Quel est l’impact réel des pipelines en échec ? \n\nEn plus de retarder votre déploiement, un pipeline qui échoue devient une source de stress et de frustration. Les équipes sont obligées d'interrompre leur travail pour s'atteler à la résolution du problème, ce qui multiplie les perturbations. Alors que le respect des délais est compromis, l'équipe est soumise à une pression accrue. Mais pourquoi le dépannage manuel est-il si stressant ?\n\n### Dépannage manuel \n\nLe temps nécessaire pour corriger un pipeline défaillant peut varier en fonction de différents critères, tels que :\n- La connaissance du projet par l’équipe de développement\n- Son expérience avec des problèmes similaires\n- Ses compétences globales en matière de résolution de problèmes\n\nIl est à la fois éprouvant et fastidieux d'analyser manuellement les logs pour comprendre ce qui s'est passé. Les logs proviennent généralement de diverses sources, y compris d'erreurs d'application et de messages système. Ils sont en outre souvent mal structurés et donc difficiles à interpréter. Les équipes doivent alors constamment basculer entre les nombreuses tâches pour corriger le pipeline, ce qui ralentit encore le processus.\n\nAvec [GitLab Duo](https://about.gitlab.com/fr-fr/gitlab-duo/), les équipes de développement peuvent passer au crible toutes ces données éparpillées et repérer les problèmes beaucoup plus rapidement. Grâce à ce processus simplifié, vous n'avez pas besoin d'une expertise approfondie pour comprendre ce qui s'est passé. Avec l'IA, la réparation de vos pipelines devient plus rapide, plus facile et beaucoup moins stressante.\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176104/Blog/zxvvu7p9vc3qpmwl32ya.png\" alt=\"broken pipeline\">\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176108/Blog/bpx6dqilfhltzboyp8k8.png\" alt=\"fix suggestions for broken pipelines\">\n\n## L'analyse des causes profondes de GitLab Duo avec l'IA générative\n\nSi votre pipeline CI/CD échoue, vous n'avez plus besoin de consacrer des heures à le dépanner manuellement. Recourez simplement à l'[analyse des causes profondes (Root Cause Analysis) de GitLab Duo](https://docs.gitlab.com/user/gitlab_duo_chat/examples/#troubleshoot-failed-cicd-jobs-with-root-cause-analysis). Cette fonctionnalité, alimentée par l'IA, identifie rapidement la cause exacte de l'échec de votre pipeline CI/CD et suggère des correctifs, directement au sein de la plateforme [DevSecOps](https://about.gitlab.com/fr-fr/topics/devsecops/ \"Qu'est-ce que DevSecOps ?\") de GitLab. Peu importe la longueur ou la complexité de vos traces de piles d'appels, la fonctionnalité  d’analyse des causes profondes de GitLab Duo analyse toutes les données en détail, puis vous fournit des informations claires et exploitables.\n\n**Cette fonctionnalité vous indique la cause exacte de l'échec, propose des corrections et identifie même les fichiers et les lignes de code spécifiques qui nécessitent une attention particulière.** Pour vous faciliter encore plus la tâche, il suggère des correctifs de code pour rendre votre pipeline de nouveau fonctionnel. Le dépannage devient ainsi beaucoup plus rapide et plus simple.\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\nEn plus de vous apporter des réponses, l'analyse des causes profondes de GitLab Duo vous permet également de poser des questions complémentaires pour en savoir plus sur les raisons de cet échec. Vous souhaitez explorer des solutions alternatives ? Aucun problème. Il vous suffit d'[ajouter du contexte](https://docs.gitlab.com/ee/user/gitlab_duo_chat/index.html#the-context-chat-is-aware-of) à votre requête en faisant référence à d'autres fichiers, tickets ou epics de votre dépôt. Par exemple, vous pouvez ouvrir votre fichier `.gitlab-ci.yml` dans l'IDE et poser la question suivante dans le chat : « Sur la base de ce fichier et du pipeline CI/CD analysé, comment proposeriez-vous d'optimiser le pipeline ? »\n\n## La confidentialité avant tout : vos données restent dans GitLab\nLa fonctionnalité d'analyse des causes profondes de GitLab Duo est prête à l'emploi sur la plateforme, ce qui représente un atout majeur. Vous n'avez pas à basculer vers un autre outil ou à chercher de l'aide extérieure. De plus, vos [logs et vos données sensibles restent sécurisés](https://about.gitlab.com/fr-fr/privacy/), car vous n'avez pas besoin de les envoyer à des solutions d'IA externes. Parfaitement intégrée à la plateforme GitLab, l'analyse des causes profondes de GitLab Duo offre des informations précieuses sans jamais compromettre la confidentialité de vos données.\n\n![pipelines en échec - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097363/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097362773.png)\n\n## Lancez-vous dès aujourd'hui\n\nDécouvrez comment l'IA peut optimiser et accélérer votre processus de développement logiciel. Suivez notre présentation produit de GitLab Duo Enterprise ci-dessous et découvrez comment les informations générées par l'IA à l'aide de GitLab Duo peuvent transformer chaque étape de votre cycle de développement logiciel, de la planification au déploiement, en passant par le codage et le dépannage. Cliquez sur l'image ci-dessous pour lancer la démo !\n\n[![Présentation de GitLab Duo Enterprise](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> [Commencez votre essai gratuit de 60 jours de GitLab Duo dès aujourd'hui !](https://about.gitlab.com/fr-fr/solutions/gitlab-duo-pro/sales/)","ai-ml",[23,24,25,26],"AI/ML","DevSecOps platform","tutorial","features","2025-02-17",{"slug":29,"featured":6,"template":30},"quickly-resolve-broken-ci-cd-pipelines-with-ai","BlogPost","content:fr-fr:blog:quickly-resolve-broken-ci-cd-pipelines-with-ai.yml","yaml","Quickly Resolve Broken Ci Cd Pipelines With 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