[{"data":1,"prerenderedAt":1032},["ShallowReactive",2],{"/fr-fr/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity":3,"footer-fr-fr":35,"the-source-banner-fr-fr":346,"the-source-navigation-fr-fr":358,"the-source-newsletter-fr-fr":385,"footer-source-/fr-fr/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity/":396,"article-site-categories-fr-fr":402,"how-ai-helps-devsecops-teams-improve-productivity-article-hero-category-fr-fr":404,"how-ai-helps-devsecops-teams-improve-productivity-the-source-source-cta-fr-fr":428,"how-ai-helps-devsecops-teams-improve-productivity-category-fr-fr":444,"how-ai-helps-devsecops-teams-improve-productivity-the-source-resources-fr-fr":457,"how-ai-helps-devsecops-teams-improve-productivity-article-hero-author-fr-fr":483},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":12,"content":18,"type":26,"category":5,"slug":27,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/fr-fr/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity","ai",false,"",{"layout":9,"template":10,"author":11,"featured":6},"the-source","TheSourceArticle","kristina-weis",{"title":13,"description":14,"ogImage":15,"config":16},"Productivité des équipes DevSecOps : comment l'améliorer grâce à l'IA","Découvrez comment les équipes DevOps peuvent travailler plus rapidement et augmenter leur productivité grâce à l'IA.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464559/fbvzbz6vxppsblv8sngf.png",{"ignoreTitleCharLimit":17},true,{"title":13,"date":19,"description":14,"timeToRead":20,"heroImage":15,"keyTakeaways":21,"articleBody":25},"2024-01-02","Lecture : 6 min",[22,23,24],"L'utilisation de l'IA dans les workflows DevSecOps améliore considérablement l'efficacité et la productivité des équipes en automatisant les tâches répétitives, en réduisant le changement de contexte et en fournissant une assistance intelligente.","Les outils d'IA renforcent la qualité et la sécurité du code en offrant des suggestions en temps réel, un résumé des potentielles menaces ainsi que des stratégies de correction.","Les entreprises doivent mettre en place des stratégies claires pour gérer les enjeux de confidentialité, de propriété intellectuelle et de sécurité des données, garantissant ainsi une adoption de l'IA conforme aux normes légales et éthiques.","L'intelligence artificielle (IA) et le machine learning (ML) jouent un rôle de plus en plus important dans le développement logiciel. Les équipes DevSecOps en tirent parti de multiples façons pour fluidifier les workflows, améliorer leur productivité et gagner en efficacité. \n\nVoici comment les équipes de développement, de sécurité et des opérations peuvent intégrer l'IA à leurs processus DevOps.\n\n## 9 façons d'utiliser l'IA dans l'approche DevSecOps\n\n### 1. Accéder plus rapidement à la documentation grâce aux chatbots IA\nPour trouver des réponses plus rapidement et limiter le changement de contexte, les équipes DevSecOps peuvent s'appuyer sur des chatbots alimentés par l'IA et poser toutes leurs questions. Elles obtiennent en retour des réponses pertinentes, en temps réel, directement à partir de la documentation ou d'autres sources volumineuses. Au lieu de quitter leur environnement de développement (IDE) ou leur plateforme pour effectuer une recherche sur Internet, les développeurs peuvent interroger un chatbot intégré et obtenir une réponse concise sans interrompre leur workflow.\n\n### 2. Automatiser la suggestion de tests et générer des fichiers de test\nL'IA peut non seulement suggérer des tests, mais aussi créer automatiquement les fichiers de test du code, directement dans une merge request. Cette automatisation permet aux développeurs d'améliorer la qualité des tests, de s'assurer que les nouvelles modifications apportées au code sont bien couvertes par des tests adéquats, tout en réduisant le temps consacré à la rédaction et à la conception des tests.\n\n### 3. Résumer les modifications apportées au code\nLors des validations ou des merge requests, les développeurs peuvent utiliser l'IA pour générer un résumé textuel clair des modifications apportées au code, et ainsi gagner du temps et simplifier les revues de code. L'IA facilite le travail des relecteurs en leur fournissant un contexte pertinent avant même qu'ils n'examinent le code en détail.\n\n### 4. Obtenir des suggestions de relecteurs de code\nLa revue de code est un processus important, mais parfois frustrant et fastidieux, surtout si le bon relecteur n'est pas sollicité dès le départ.\n\nEn analysant les modifications apportées au code ainsi que le graphique des contributeurs du projet, l'IA peut automatiquement recommander un relecteur qualifié, capable de fournir des commentaires précis rapidement et d'identifier les problèmes potentiels. Elle peut également offrir un réel gain de temps en suggérant une autre personne pour la revue de code si le premier relecteur suggéré n'est pas disponible ou si sa revue de code n'est pas assez détaillée.\n\n### 5. Résumer les discussions\nLorsque les échanges entre les parties prenantes au projet deviennent trop longues ou complexes, l'IA peut synthétiser les commentaires autour d'un problème ou au sein d'un ticket. Cela permet aux équipes de mieux comprendre le statut d'un projet et les prochaines étapes. Cette approche favorise une collaboration plus fluide et une prise de décision plus rapide.\n\n### 6. Suggérer du code en temps réel\n[Les suggestions de code alimentées par l'IA](https://about.gitlab.com/blog/top-tips-for-efficient-ai-powered-code-suggestions-with-gitlab-duo/) aident les développeurs à coder plus efficacement en complétant les blocs de code directement dans leur IDE. L'IA peut également définir la logique des déclarations de fonction et générer un brouillon de son implémentation, créer des tests unitaires, suggérer du code commun comme des motifs d'expressions régulières (regex) et bien plus encore. Il est évident que ces fonctionnalités permettent aux développeurs de gagner en efficacité. Cependant, comme le montre [notre enquête](https://about.gitlab.com/developer-survey/), les développeurs consacrent moins de 25 % de leur temps au développement de code, ce gain d'efficacité est donc minime.\n\n### 7. Expliquer le fonctionnement d'une portion de code\nLes développeurs, tout comme les autres membres composant l'équipe DevOps, peuvent utiliser l'IA pour obtenir une explication rapide de la fonction et du comportement d'un bloc de code, et ce sans quitter leur workflow.\n\nUne explication du code générée par l'IA peut s'avérer particulièrement utile lorsqu'ils tentent de comprendre des éléments de code créés par d'autres ou rédigés dans un langage qu'ils maîtrisent moins. Par ailleurs, selon [notre enquête](https://about.gitlab.com/developer-survey/), les développeurs consacrent 13 % de leur temps à analyser le fonctionnement du code, de sorte que les gains de temps pour ce type de tâche sont incontestables.\n\n### 8. Résumer les vulnérabilités détectées dans le code\nComprendre une faille de sécurité qui vient d'être détectée et savoir comment la corriger n'est pas une mince affaire. Les outils de sécurité alimentés par l'IA simplifient et optimisent cette tâche. Un [résumé des vulnérabilités généré par l'IA](https://about.gitlab.com/blog/developing-gitlab-duo-use-ai-to-remediate-security-vulnerabilities/) aide les développeurs et les professionnels de la sécurité à comprendre la nature de la vulnérabilité, ses potentielles exploitations, ainsi que les mesures correctives nécessaires. Certains outils d'IA vont même plus loin et suggèrent des mesures d'atténuation des risques par le biais d'un exemple de correctif sous forme de code. Cette fonctionnalité peut grandement [aider les équipes à éviter plus facilement les menaces et risques de sécurité potentiels](https://about.gitlab.com/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security/).\n\n### 9. Prévoir les indicateurs de productivité\nGrâce à l'IA, les responsables DevSecOps peuvent [surveiller et anticiper les indicateurs de productivité clés](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/), tels que la fréquence des déploiements, afin d'identifier les tendances et les anomalies tout au long du cycle de développement logiciel. Les informations exploitables obtenues permettent aux équipes de mettre en œuvre les améliorations nécessaires à leurs processus DevSecOps pour optimiser leur productivité.\n\n## Les avantages d'intégrer l'IA dans le développement logiciel \nLes équipes DevSecOps qui utilisent déjà l'IA, tout comme celles qui prévoient de l'utiliser, peuvent accomplir de nombreuses tâches, notamment :\n\n* Améliorer l'efficacité de leur cycle de développement logiciel\n* Accélérer la durée du cycle\n* Simplifier les contrôles de conformité\n* Augmenter la productivité des employés\n* Améliorer la posture de sécurité\n* Optimiser la qualité du code\n* Accroître la satisfaction des clients\n* Améliorer la satisfaction des employés et l'expérience développeur\n* Faciliter la collaboration entre les équipes\n* Améliorer les performances des applications\n* Automatiser les tâches répétitives\n* Réduire les coûts opérationnels \n* Réduire le changement de contexte et la charge cognitive\n* Réduire les erreurs humaines\n* Accélérer l'intégration des nouveaux employés\n* Aider les employés à [apprendre de nouveaux langages de programmation](https://about.gitlab.com/blog/learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions/)\n\n## Prévenir les risques liés à la confidentialité et à la sécurité\nBien que l'IA apporte de nombreux avantages au processus de développement logiciel, il est important d'anticiper les risques, défis et obstacles courants liés à son intégration.\n\nSelon notre [enquête](https://about.gitlab.com/developer-survey/2024/ai/), la confidentialité, la sécurité et le manque de familiarité avec les solutions basées sur l'IA sont des obstacles courants que les professionnels DevSecOps interrogées ont déclaré avoir rencontrés ou s'attendre à rencontrer lors de la mise en œuvre de l'IA dans le cycle de développement logiciel. Parmi tous les obstacles identifiés, les préoccupations concernant la confidentialité et la sécurité des données ont été la réponse la plus courante (34 %), suivie du manque de compétences appropriées (31 %) et du manque de connaissances en matière d'IA (30 %).\n\nLes dirigeants d'entreprise doivent s'assurer que leur implémentation de l'IA respecte les normes de confidentialité et de sécurité établies. Cela implique d'intégrer des mécanismes de contrôle et de validation de la conformité tout au long du cycle de vie de l'IA afin de protéger les données sensibles et maintenir la confiance des utilisateurs. Il est également essentiel pour une entreprise de s'assurer qu'elle adopte des outils d'IA offrant une transparence totale sur la façon dont leurs modèles de machine learning utilisent les données.\n\n## Découvrez GitLab Duo\nToutes les fonctionnalités mentionnées ci-dessus, des explications de code aux suggestions de tests, sont incluses dans [GitLab Duo](https://about.gitlab.com/gitlab-duo/), la suite de fonctionnalités d'IA intégrée à la plateforme DevSecOps de GitLab. 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