一句话：探讨LLM自主智能体在软件工程中的能力与挑战，并发现上下文丰富提示能提升需求理解。
随着配备大语言模型（LLM）的自主智能体在软件工程中迅速兴起，本文考察其当前能力、挑战与机遇。作者使用由 GPT-3.5 和 GPT-4 驱动的开源应用 Auto-GPT（v0.3.0），设计含上下文与不含上下文的不同提示进行探索。结果表明，上下文丰富的提示有助于智能体理解用户需求，避免无关细节干扰任务理解并降低模型性能；这一优势在处理 Spring Boot、Django、Flask 等复杂框架时尤为明显。文章据此讨论自主智能体在软件工程实践中的潜力与局限。
Autonomous agents equipped with Large Language Models (LLMs) are rapidly gaining prominence as a revolutionary technology within the realm of Software Engineering. These intelligent and autonomous systems demonstrate the capacity to perform tasks and make independent decisions, leveraging their intrinsic reasoning and decision-making abilities. This paper delves into the current state of autonomous agents, their capabilities, challenges, and opportunities in Software Engineering practices. By employing different prompts (with or without context), we conclude the advantages of contextrich prompts for autonomous agents. Prompts with context enhance user requirement understanding, avoiding irrelevant details that could hinder task comprehension and degrade model performance, particularly when dealing with complex frameworks such as Spring Boot, Django, Flask, etc. This exploration is conducted using Auto-GPT (v0.3.0), an open-source application powered by GPT-3.5 and GPT-4 which intelligently connects the “thoughts” of Large Language Models (LLMs) to independently accomplish the assigned goals or tasks.
@inproceedings{SuriDSDSK23,
title = {Software Engineering Using Autonomous Agents: Are We There Yet?},
author = {Samdyuti Suri and Sankar Narayan Das and Kapil Singi and Kuntal Dey and Vibhu Saujanya Sharma and Vikrant Kaulgud},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00174}
}