ARKA UÇ YAZILIM PROJELERI İÇIN ÜRETKEN YAPAY ZEKA TABANLI, SÖZLEŞME ODAKLI OTOMATİK BIRIM TESTİ BAKIM YAKLAŞIMI

Öz

Güncel arka uç sistemlerinde fonksiyon sözleşmeleri ve API yüzeyleri sık sık değişmekte, bu da birim testlerinin hızla geçerliliğini yitirmesine neden olmaktadır. Mevcut araçların büyük bölümü ilk test üretimine odaklanmakta, test bakımının önemli bir kısmı ise hâlâ manuel yürütülmektedir. Bu çalışma, sözleşme değişikliklerini kaynak düzeyinde algılayan ve ilişkili Jest testlerini büyük dil modelleri (LLM) ile yeniden üreten sözleşme-tabanlı bir bakım yaklaşımı sunmaktadır. Önerilen yöntem, TypeScript tabanlı dört açık kaynak arka uç projesinde toplam 28 sözleşme değişimi üzerinde değerlendirilmiştir. Deneyler, aracın başarısız koşulları otomatik olarak onarabilen, kısmi iyileştirmeler sağlayabilen ve mevcut test altyapısıyla bütünleşebilen pratik bir “self-healing” mekanizması sunduğunu göstermektedir. Bulgular, yöntemin başarısının proje mimarisi ve test suite kalitesiyle yakından ilişkili olduğunu da ortaya koymaktadır.

Anahtar Kelimeler

• Birim testi
• test bakımı
• yapay zeka
• sözleşme değişimi
• typescript
• yazılım evrimi

A CONTRACT-DRIVEN AUTOMATED UNIT TEST MAINTENANCE APPROACH WITH GENERATIVE ARTIFICIAL INTELLIGENCE FOR BACKEND SOFTWARE PROJECTS

Abstract

Modern backend systems frequently undergo changes in function contracts and API surfaces, which can quickly render unit tests outdated, brittle, or unusable. Most existing tools—whether based on classical test generation or large language models (LLMs)—focus on initial test creation, leaving the ongoing maintenance of existing test suites largely manual and error-prone. This paper presents a contract-driven, AI-assisted framework for unit test maintenance in TypeScript backend projects. The framework detects function-level contract changes and adapts related Jest tests through small, validated edits synthesized by an LLM, without creating new tests or performing broad refactorings. We evaluate the approach on 28 contract-change instances across four open-source projects. The results indicate that contract-aware, LLM-based test maintenance can act as a practical self-healing mechanism when contract changes are visible in the test surface, while its effectiveness remains strongly shaped by project architecture and test-suite design.

Keywords

• Unit testing
• test maintenance
• artificial intelligence
• contract changes
• TypeScript
• software evolution

Kaynakça

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