Pith Number

pith:ONRDOB34

pith:2024:ONRDOB345KFXIPZDCKNAK3YQJK

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

Junwei Liu, Kaixin Wang, Lingming Zhang, Xin Peng, Yiling Lou, Yixuan Chen, Zhenpeng Chen

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

arxiv:2409.02977 v2 · 2024-09-04 · cs.SE · cs.AI

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That the 124 collected papers form a representative sample of the field and that the chosen two-perspective categorization sufficiently captures the essential distinctions without significant omissions or overlaps.

C3one line summary

A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.

References

290 extracted · 290 resolved · 13 Pith anchors

[1] A Survey of Large Language Models 2023 · arXiv:2303.18223

[2] Large language models for software engineering: A systematic literature review.ACM Trans 2024

[3] Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M. Zhang. Large language models for software engineering: Survey and open problems. In IEEE/ACM Internation 2023

[4] Self-collaboration code generation via chatgpt.ACM Trans 2024

[5] Evaluating the code quality of AI-assisted code generation tools: An empirical study on GitHub Copilot, Amazon Code- Whisperer, and ChatGPT 2023

Formal links

2 machine-checked theorem links

Cited by

19 papers in Pith

The Command Line GUIde: Graphical Interfaces from Man Pages via AI

Code Semantic Zooming

EvoDev: An Iterative Feature-Driven Framework for End-to-End Software Development with LLM-based Agents

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents

Receipt and verification

First computed	2026-05-17T23:38:14.096973Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620

Aliases

arxiv: 2409.02977 · arxiv_version: 2409.02977v2 · doi: 10.48550/arxiv.2409.02977 · pith_short_12: ONRDOB345KFX · pith_short_16: ONRDOB345KFXIPZD · pith_short_8: ONRDOB34

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ONRDOB345KFXIPZDCKNAK3YQJK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 736237077cea8b743f23129a056f104a9f2e003b65d320fa47932fcb3a40c620

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5587aa4f1c01310a73ea5f4115aa53ba7ae2409d631937337c93ca25217bf94a",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.SE",
    "submitted_at": "2024-09-04T15:59:41Z",
    "title_canon_sha256": "810cc8f9984bd735b2c41c993d66cced9a41c4e023da8047ac40bb7015218286"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2409.02977",
    "kind": "arxiv",
    "version": 2
  }
}