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arxiv: 2409.02977 · v2 · pith:ONRDOB34new · submitted 2024-09-04 · 💻 cs.SE · cs.AI

Large Language Model-Based Agents for Software Engineering: A Survey

Junwei Liu , Kaixin Wang , Yixuan Chen , Xin Peng , Zhenpeng Chen , Lingming Zhang , Yiling Lou This is my paper

Pith reviewed 2026-05-17 12:29 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords LLM-based agentsSoftware EngineeringSurveyLarge Language ModelsAI AgentsMulti-agent SystemsSoftware Development
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0 comments X

The pith

This survey gathers 124 papers on LLM-based agents for software engineering and sorts them by software engineering tasks and agent structures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish a clear map of the emerging field of LLM-based agents applied to software engineering. It does this by collecting 124 papers and dividing them according to two viewpoints: one focused on the software engineering activities involved and the other on the design and capabilities of the agents. A sympathetic reader would care because these agents go beyond plain language models by adding perception and tool use, which opens new ways to handle complex real-world development issues through collaboration between agents and humans. The survey wraps up by pointing out open challenges and possible next steps.

Core claim

The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 papers and categorize them from two the SE

What carries the argument

The two-perspective categorization system that organizes papers according to software engineering tasks on one side and agent architectures and interactions on the other.

If this is right

  • Developers gain a structured way to find relevant work on using agents for specific SE activities like coding or testing.
  • Insights into how agent collaboration and human-in-the-loop setups can address more difficult problems in software development.
  • Identification of gaps that point toward research on improving agent reliability and integration with existing SE tools.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such a categorization might help in creating taxonomies that could be applied to LLM agents in other engineering domains beyond software.
  • Future surveys could track how the field evolves by updating the paper list and reapplying the same perspectives.
  • The emphasis on external resources and tools suggests potential for agents that integrate with version control systems or testing frameworks in novel ways.

Load-bearing premise

The 124 papers collected represent the main body of work in this area without major omissions and the chosen categorization from SE and agent perspectives covers the key distinctions without significant overlaps or missing categories.

What would settle it

A review of recent publications that reveals many important papers on LLM-based agents in software engineering that were not included in the survey or that do not align well with either the SE or agent perspective categories.

read the original abstract

The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 124 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. The repository of this survey is at https://github.com/FudanSELab/Agent4SE-Paper-List.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper presents a survey on LLM-based agents for Software Engineering. It collects 124 papers from the literature and categorizes them using two perspectives: an SE perspective (covering tasks such as requirements, design, coding, testing, and maintenance) and an agent perspective (covering components such as perception, planning, memory, and tool use, along with multi-agent and human-in-the-loop setups). The survey also identifies open challenges and outlines future directions, accompanied by a public GitHub repository listing the papers.

Significance. If the paper collection is shown to be representative and the dual categorization is applied consistently without major gaps or overlaps, the survey would provide a useful map of an emerging interdisciplinary area. The public repository strengthens reproducibility and allows the community to extend the list. However, the overall significance is limited by the absence of a documented, reproducible selection protocol, which is a standard requirement for systematic surveys in this field.

major comments (2)
  1. [Section 2] Collection methodology (Section 2): The claim of a 'comprehensive and systematic survey' rests on the collection of 124 papers, yet no search strings, databases (arXiv, ACM DL, IEEE Xplore, etc.), date range, or inclusion/exclusion criteria are stated. This omission prevents verification that the sample is representative and free of venue or temporal bias.
  2. [Section 4] Categorization framework (Section 4): The two-perspective taxonomy is presented as the core organizational device, but the manuscript provides no explicit discussion or examples of how papers that span multiple SE tasks and agent features are assigned, nor any check for category overlap or unclassified work. Without such validation, the taxonomy's completeness and non-redundancy cannot be assessed.
minor comments (3)
  1. [Abstract] The abstract would benefit from a single sentence stating the time window of the literature search.
  2. [Figure 1] Figure 1 (or the taxonomy diagram) should include a small number of concrete paper examples placed in each leaf category to illustrate classification decisions.
  3. [Repository] The GitHub repository is a clear asset; adding a last-updated date and a brief description of how new papers will be incorporated would further improve its utility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our survey. We address each major comment below and will revise the manuscript to enhance methodological transparency and taxonomy clarity.

read point-by-point responses

  1. Referee: [Section 2] Collection methodology (Section 2): The claim of a 'comprehensive and systematic survey' rests on the collection of 124 papers, yet no search strings, databases (arXiv, ACM DL, IEEE Xplore, etc.), date range, or inclusion/exclusion criteria are stated. This omission prevents verification that the sample is representative and free of venue or temporal bias.

    Authors: We acknowledge that the current manuscript does not provide an explicit description of the collection protocol in Section 2. In the revision, we will add a dedicated subsection detailing the search process: databases queried include arXiv, Google Scholar, ACM Digital Library, and IEEE Xplore; search strings combined terms such as 'LLM-based agent' with SE task keywords (e.g., 'requirements engineering', 'code generation', 'testing'); the time range covers January 2022 to August 2024 to capture the post-ChatGPT emergence of the topic; and inclusion criteria require papers to propose, implement, or evaluate LLM agents for concrete SE tasks, while excluding standalone LLM studies without agent or SE focus and non-English publications. This addition will allow independent verification of representativeness. The existing public GitHub repository will be updated with the full search log and paper metadata to support reproducibility. revision: yes

  2. Referee: [Section 4] Categorization framework (Section 4): The two-perspective taxonomy is presented as the core organizational device, but the manuscript provides no explicit discussion or examples of how papers that span multiple SE tasks and agent features are assigned, nor any check for category overlap or unclassified work. Without such validation, the taxonomy's completeness and non-redundancy cannot be assessed.

    Authors: We agree that the manuscript would benefit from explicit guidance on taxonomy application. We will insert a new paragraph in Section 4 describing the assignment rules: each paper is classified by its primary SE task (determined by the core empirical contribution) and primary agent component (e.g., planning when reasoning chains dominate), with secondary aspects noted via cross-references or table footnotes. We will provide three concrete examples of multi-category papers and explain their placement. We will also state that all 124 collected papers fit within the taxonomy after review, with no unclassified items, and briefly discuss how the hierarchical structure reduces overlap. These additions will allow readers to evaluate completeness and non-redundancy. revision: yes

Circularity Check

0 steps flagged

No circularity: survey reports external literature without derivations or self-referential reductions

full rationale

This is a survey paper that collects 124 external papers from the literature and organizes them under two perspectives (SE and agent). It contains no equations, parameter fittings, predictions, or derivations that could reduce to the paper's own inputs by construction. The central claim of comprehensiveness is a descriptive assertion about the collection process rather than a mathematical or fitted result; no self-citation chain or ansatz is used to justify any quantitative output. Per the guidelines, a self-contained descriptive survey against external benchmarks receives score 0 with no steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Surveys rest primarily on domain assumptions about literature coverage and the utility of the chosen taxonomy.

axioms (1)
  • domain assumption The 124 papers identified through the authors' search constitute a sufficiently complete and unbiased sample of relevant LLM-agent SE research.
    Stated implicitly by the claim of a 'comprehensive and systematic survey' without detailed search protocol in the abstract.

pith-pipeline@v0.9.0 · 5463 in / 1086 out tokens · 39330 ms · 2026-05-17T12:29:16.894990+00:00 · methodology

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