Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Consequently, generated artifacts may exhibit brittle behavior and limited deployability. We propose a retrieval-augmented scaffolding approach that combines platform-based code generation with agentic clarification loops to expose and resolve architectural constraint ambiguities. By combining template retrieval with structured interaction, the method embeds production-relevant considerations during service scaffolding. Evaluation indicates improved architectural consistency and deployability compared to general-purpose AI code generation workflows, suggesting that constraint-aware retrieval is essential for aligning AI-assisted service development with production software engineering practices.
Code (0)
등록된 구현이 없습니다.
Tasks
Code GenerationSimilar Papers 제목 키워드 기반
Hardware-Assisted Virtualization of Neural Processing Units for Cloud Platforms
Cloud platforms today have been deploying hardware accelerators like neural processing units (NPUs) for powering machine learning (ML) inference services. To maximize the resource utilization while ensuring reasonable qu…
SchedulingArchitectural Implications of Function-as-a-Service Computing
Serverless computing is a rapidly growing cloud application model, popularized by Amazon’s Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user de…
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning
Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide compre…
Multiple Instance LearningaiXamine: Simplified LLM Safety and Security
Evaluating Large Language Models (LLMs) for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address …
2kAdversarial RobustnessFairnessHallucination+2Human-in-the-Loop Control of Objective Drift in LLM-Assisted Computer Science Education
Large language models (LLMs) are increasingly embedded in computer science education through AI-assisted programming tools, yet such workflows often exhibit objective drift, in which locally plausible outputs diverge fro…
Code Generation