用AI构建AI密集型软件：早期成果及关于测量开发成本的警示故事
Building AI-Intensive Software with AI: Early Results and a Cautionary Tale on Measuring Development Cost
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中文总结 AI 辅助
本文通过六人学生团队用AI构建对话式入职助手的案例研究，修正了AI密集型软件开发成本测量的两个常见错误，得出修正后的成本比率约为9.9倍，并提出需建立更稳健的成本核算方法。
中文摘要 AI 辅助
关于AI密集型软件开发真实成本的实证报告仍然稀缺，而少数已有的报告很容易出现错误，这些错误永远不会在最终数字中显现。我们报告了一项正在进行的案例研究的早期成果：一个六人学生团队在一个学期内，利用广泛的AI辅助构建了一个完整的对话式入职助手——基于RAG的代码聊天、引导式游览、依赖关系图、技术债务分析。我们使用三层成本模型（实际AI支出、自我报告的人力投入、人力反事实成本）对开发过程进行了检测，最初报告的成本比率为19.4倍。后续检查发现了两个独立的错误——在统一费率订阅下推断每token成本，以及使用错误的地区劳动力成本来定价反事实成本——这两个错误共同使比率膨胀了约2倍；修正后的数字约为9.9倍。我们将这一修正本身作为一项早期的、可推广的发现提出：这两个错误很容易犯，在最终数字中不可见，并且在类似报告中可能很常见。我们概述了迈向更稳健、可复制的AI密集型开发成本核算方法的后续步骤。
英文摘要
Empirical reports on the true cost of AI-intensive software development remain scarce, and the few that exist are easy to get wrong in ways that never surface in the final number. We report early results from an ongoing case study: a six-person student team built a full conversational onboarding assistant -- RAG-based code chat, guided tours, dependency graphs, technical-debt analysis -- over one academic term using pervasive AI assistance. We instrumented development with a three-layer cost model (real AI spend, self-reported human effort, human counterfactual) and initially reported a 19.4x cost ratio. A follow-up pass revealed two independent errors -- inferring per-token cost under a flat-rate subscription, and pricing the counterfactual with the wrong regional labor rates -- that together had inflated the ratio by roughly 2x; the corrected figure is ~9.9x. We present this correction as an early, generalizable finding in its own right: both errors are easy to make, invisible in the final number, and plausibly common in similar reports. We outline next steps toward a more robust, replicable costing methodology for AI-intensive development.
发表机构
- Centro de Informática, Universidade Federal de Pernambuco（伯南布哥联邦大学信息中心）
机构由 AI 辅助整理，请以论文原文为准。
