ÇEVİK YAZILIM GELİŞTİRME VE DEVOPS SÜREÇLERİNDE ÜRETKEN YAPAY ZEKÂ ENTEGRASYONU: ETKİ MEKANİZMALARI, UYGULAMA DESENLERİ VE ÖLÇÜM METRİKLERİ

Öz

Üretken yapay zekânın yazılım mühendisliğine etkisi, tekil görev başarısından öte süreç düzeyinde incelenmesi gereken bir araştırma alanına dönüşmüştür. Bu çalışma, 2016-2026 dönemine ait Scopus, Web of Science, ScienceDirect, ACM Digital Library, IEEE Xplore ve Türkiye Atıf Dizini (TR Dizin) kaynaklı literatürü yapılandırılmış inceleme ve tematik sentez yöntemiyle ele almaktadır. DOI doğrulaması yapılan ve çevik geliştirme ya da DevOps süreçleriyle doğrudan ilişkili elli bir çalışma örnekleme alınmıştır. Bulgular üç eksen üzerinde yoğunlaşmaktadır: düşük belirsizlikli görevlerde —kod taslağı, birim test üretimi ve backlog genişletme— tutarlı verimlilik kazanımları; güvenlik, doğruluk ve teknik borç boyutunda karışık kanıtlar; geliştirici benimsemesinin kurumsal düzeyde aynı hızla ilerlemediği yönetişim açıkları. Kodlama yardımcısına erişimli geliştiricilerin daha yüksek oranda güvenlik açığı ürettiğine ilişkin deney bulguları, hız-kalite dengesinin tek boyutlu üretkenlik göstergesiyle temsil edilemeyeceğini göstermektedir. İnceleme sonunda teslim hızı, kalite, güvenlik, teknik borç ve kullanıcı kabulünü birlikte izleyen çok boyutlu bir ölçüm çerçevesi önerilmektedir.

Anahtar Kelimeler

• Üretken Yapay Zekâ
• Çevik Yazılım Geliştirme
• DevOps
• Yazılım Mühendisliği
• Ölçüm Metrikleri

INTEGRATING GENERATIVE ARTIFICIAL INTELLIGENCE INTO AGILE SOFTWARE DEVELOPMENT AND DEVOPS PROCESSES: EFFECT MECHANISMS, APPLICATION PATTERNS, AND MEASUREMENT METRICS

Öz

The influence of generative artificial intelligence on software engineering has evolved from isolated task-level performance into a process-level research question of considerable scope. This study synthesizes literature from Scopus, Web of Science, ScienceDirect, ACM Digital Library, IEEE Xplore, and the TR Dizin index for the period 2016–2026, applying a structured review and thematic coding methodology. Fifty-one studies with verified DOIs and direct relevance to agile development or DevOps processes were retained. The findings converge on three themes: consistent productivity gains in low-uncertainty tasks such as code drafting, unit test generation, and backlog expansion; mixed evidence on correctness, security, and technical debt; and governance gaps in which individual tool adoption does not transfer to organizational process integration. Evidence from randomised user studies demonstrates that developers with AI assistant access produce code containing more security vulnerabilities and hold stronger beliefs that their code is secure, establishing that speed gains cannot be represented by a single productivity indicator. The review concludes by proposing a multidimensional measurement frame that jointly monitors delivery speed, quality, security, technical debt, and user acceptance.

Anahtar Kelimeler

• Generative Artificial İntelligence
• Agile Software Development
• DevOps
• Software Engineering
• Measurement Metrics

Kaynakça

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