Recent advancements in large language models (LLMs) have significantly
advanced the automation of software development tasks, including code
synthesis, program repair, and test generation. More recently, researchers and
industry practitioners have developed various autonomous LLM agents to perform
end-to-end software development tasks. These agents are equipped with the
ability to use tools, run commands, observe feedback from the environment, and
plan for future actions. However, the complexity of these agent-based
approaches, together with the limited abilities of current LLMs, raises the
following question: Do we really have to employ complex autonomous software
agents? To attempt to answer this question, we build Agentless -- an agentless
approach to automatically solve software development problems. Compared to the
verbose and complex setup of agent-based approaches, Agentless employs a
simplistic two-phase process of localization followed by repair, without
letting the LLM decide future actions or operate with complex tools. Our
results on the popular SWE-bench Lite benchmark show that surprisingly the
simplistic Agentless is able to achieve both the highest performance (27.33%)
and lowest cost (\$0.34) compared with all existing open-source software
agents! Furthermore, we manually classified the problems in SWE-bench Lite and
found problems with exact ground truth patch or insufficient/misleading issue
descriptions. As such, we construct SWE-bench Lite-S by excluding such
problematic issues to perform more rigorous evaluation and comparison. Our work
highlights the current overlooked potential of a simple, interpretable
technique in autonomous software development. We hope Agentless will help reset
the baseline, starting point, and horizon for autonomous software agents, and
inspire future work along this crucial direction.

通过构建一个名为 Agentless 的简洁两阶段的定位和修复过程，本研究发现在自动软件开发问题的解决中，Agentless 不仅能够获得最高的性能（27.33%），而且成本最低（0.34 美元），超过现有的所有开源软件代理。

基于 LLM 的软件工程代理程序解密

Agentless: Demystifying LLM-based Software Engineering Agents

Prompt engineering is an increasingly important skill set needed to converse
effectively with large language models (LLMs), such as ChatGPT. Prompts are
instructions given to an LLM to enforce rules, automate processes, and ensure
specific qualities (and quantities) of generated output. Prompts are also a
form of programming that can customize the outputs and interactions with an
LLM. This paper describes a catalog of prompt engineering techniques presented
in pattern form that have been applied to solve common problems when conversing
with LLMs. Prompt patterns are a knowledge transfer method analogous to
software patterns since they provide reusable solutions to common problems
faced in a particular context, i.e., output generation and interaction when
working with LLMs. This paper provides the following contributions to research
on prompt engineering that apply LLMs to automate software development tasks.
First, it provides a framework for documenting patterns for structuring prompts
to solve a range of problems so that they can be adapted to different domains.
Second, it presents a catalog of patterns that have been applied successfully
to improve the outputs of LLM conversations. Third, it explains how prompts can
be built from multiple patterns and illustrates prompt patterns that benefit
from combination with other prompt patterns.

这篇文章介绍了 Prompt 工程技术的目录，包含适用于大型语言模型的多个生产输出和互动方面的工作中的问题的重复使用解决方案。