一句话：提出基于 MCP 的 DevCoach 参考架构，用两个上下文精化循环让 GenAI 辅助具备生命周期感知、可治理与可审查性。
GenAI 在软件密集型企业中的快速、无结构化采用带来社会技术挑战：它加速个体任务，却因跨 SDLC 的协调、验证与治理开销抵消收益。本文提出基于模型上下文协议（MCP）的参考架构 DevCoach，使 GenAI 辅助更具生命周期感知、可治理与可审查。它把模型输出视为提案而非权威动作，将生成与生命周期范围内的检索、有界工具调用、确定性验证及带溯源的审计产物耦合。核心机制是两个上下文精化循环（CRL）：CRL1 把用户意图稳定为显式检索契约，CRL2 执行有界检索、证据验证与溯源构建，产出经验证的上下文包。贡献偏架构与设计，非基准实现研究，从而把 GenAI 集成重构为软件工程设计与治理问题，而非提示词级优化。
The rapid, often unstructured adoption of Generative AI (GenAI) in software-intensive organizations creates a socio-technical challenge. While GenAI can accelerate individual tasks, these gains are often offset by added coordination, verification, and governance effort across the software development lifecycle (SDLC). This paper presents DevCoach as a reference architecture built on the Model Context Protocol (MCP) to make GenAI assistance more lifecycle-aware, governable, and inspectable in organizational settings. DevCoach treats model outputs as proposals rather than authoritative actions, and couples generation to lifecycle-scoped retrieval, bounded tool use, deterministic validation, and provenance-linked audit artifacts. Its core mechanism is two Context Refinement Loops (CRLs): CRL 1 stabilizes user intent into an explicit retrieval contract, and CRL 2 performs bounded retrieval, evidence verification, and provenance construction to produce a verified context pack. The contribution is therefore architectural and design-oriented rather than a benchmarked implementation study. In this way, the paper reframes GenAI integration as a software engineering design and governance problem rather than a prompt-level optimization problem.
@inproceedings{ElsisiSM26,
title = {Lifecycle-Aware GenAI Assistance with MCP via Context Refinement Loops: A Reference Architecture},
author = {Omar Elsisi and Fabio De Abreu Santos and Glaucia Melo},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805617}
}