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

Electromechanical systems manage physical processes through a network of
inter-connected components. Today, programming the interactions required for
coordinating these components is largely a manual process. This process is
time-consuming and requires manual adaptation when system features change. To
overcome this issue, we use autonomous software agents that process semantic
descriptions of the system to determine coordination requirements and
constraints; on this basis, they then interact with one another to control the
system in a decentralized and coordinated manner.Our core insight is that
coordination requirements between individual components are, ultimately,
largely due to underlying physical interdependencies between the components,
which can be (and, in many cases, already are) semantically modeled in
automation projects. Agents then use hypermedia to discover, at run time, the
plans and protocols required for enacting the coordination. A key novelty of
our approach is the use of hypermedia-driven interaction: it reduces coupling
in the system and enables its run-time adaptation as features change.

使用自主软件代理来处理系统的语义描述，以确定协调要求和约束，并以分散协调的方式控制系统，通过超媒体发现运行时所需的计划和协议，以减少系统耦合并在特征变化时实现运行时自适应。