Agents in Software Engineering: Survey, Landscape, and Vision

• URL: http://arxiv.org/abs/2409.09030v2
• Date: Mon, 23 Sep 2024 17:55:07 GMT
• Title: Agents in Software Engineering: Survey, Landscape, and Vision
• Authors: Yanlin Wang, Wanjun Zhong, Yanxian Huang, Ensheng Shi, Min Yang, Jiachi Chen, Hui Li, Yuchi Ma, Qianxiang Wang, Zibin Zheng,
• Abstract summary: Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks. We find that many studies combining LLMs with software engineering (SE) have employed the concept of agents either explicitly or implicitly. We present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action.
• Score: 46.021478509599895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engineering (SE) field. We find that many studies combining LLMs with SE have employed the concept of agents either explicitly or implicitly. However, there is a lack of an in-depth survey to sort out the development context of existing works, analyze how existing works combine the LLM-based agent technologies to optimize various tasks, and clarify the framework of LLM-based agents in SE. In this paper, we conduct the first survey of the studies on combining LLM-based agents with SE and present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action. We also summarize the current challenges in combining the two fields and propose future opportunities in response to existing challenges. We maintain a GitHub repository of the related papers at: https://github.com/DeepSoftwareAnalytics/Awesome-Agent4SE.

Related papers

• Large Language Model-Based Agents for Software Engineering: A Survey [20.258244647363544]
  The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain.
  arXiv  Detail & Related papers  (2024-09-04T15:59:41Z)
• From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future [15.568939568441317]
  We investigate the current practice and solutions for large language models (LLMs) and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering.
  arXiv  Detail & Related papers  (2024-08-05T14:01:15Z)
• LLMs Meet Multimodal Generation and Editing: A Survey [89.76691959033323]
  This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio. We summarize the notable advancements with milestone works in these fields and categorize these studies into LLM-based and CLIP/T5-based methods. We dig into tool-augmented multimodal agents that can leverage existing generative models for human-computer interaction.
  arXiv  Detail & Related papers  (2024-05-29T17:59:20Z)
• A Survey on the Memory Mechanism of Large Language Model based Agents [66.4963345269611]
  Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems. The key component to support agent-environment interactions is the memory of the agents.
  arXiv  Detail & Related papers  (2024-04-21T01:49:46Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges [44.92286030322281]
  Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation.
  arXiv  Detail & Related papers  (2024-01-21T23:36:14Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• A Survey on Large Language Models for Software Engineering [15.468484685849983]
  Large Language Models (LLMs) are used to automate a broad range of Software Engineering (SE) tasks. This paper summarizes the current state-of-the-art research in the LLM-based SE community.
  arXiv  Detail & Related papers  (2023-12-23T11:09:40Z)
• Large Language Models for Software Engineering: A Systematic Literature Review [34.12458948051519]
  Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE) We select and analyze 395 research papers from January 2017 to January 2024 to answer four key research questions (RQs) From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and flagging promising areas for future study.
  arXiv  Detail & Related papers  (2023-08-21T10:37:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.