---
title: "ASE (A*): papers and universities"
description: "ASE (Automated Software Engineering Conference) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,102 of its papers from 2024–2026 (266 in 2024, 395 in 2025, 441 in 2026). By the countries of the authors' institutions, the most papers come from China (549), the United States (200) and Singapore (105)."
url: https://scholar4ai.com/proceedings/ase
markdown_url: https://scholar4ai.com/proceedings/ase.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ASE (Automated Software Engineering Conference) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,102 of its papers from 2024–2026 (266 in 2024, 395 in 2025, 441 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# ASE: Automated Software Engineering Conference — papers and institutions, 2024–2026

Papers of ASE, Automated Software Engineering Conference (CORE rank A*; field: Software engineering), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- ASE (Automated Software Engineering Conference) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,102 of its papers from 2024–2026 (266 in 2024, 395 in 2025, 441 in 2026).
- By the countries of the authors' institutions, the most papers come from China (549), the United States (200) and Singapore (105).
- The institutions with the most papers are Zhejiang University (64), Nanjing University (58) and Peking University (53).
- Its most-cited paper of these years is “On the Evaluation of Large Language Models in Unit Test Generation” (2024), with 89 citations.

## Papers per year

*ASE papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 266 | 264 |
| 2025 | 395 | 385 |
| 2026 | 441 | 436 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363608212](https://openalex.org/S4363608212) | The conference's proceedings |

## Who publishes there

*Institutions with the most ASE papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 64 |
| 2 | [Nanjing University](https://scholar4ai.com/institutions/nanjing-university.md) | 58 |
| 3 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 53 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 50 |
| 5 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 49 |
| 6 | Singapore Management University | 46 |
| 7 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 43 |
| 8 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 42 |
| 9 | Huawei Technologies (China) | 38 |
| 10 | [Huazhong University of Science and Technology](https://scholar4ai.com/institutions/huazhong-university-of-science-and-technology.md) | 32 |

*Countries with the most ASE papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 549 |
| 2 | United States | 200 |
| 3 | Singapore | 105 |
| 4 | Germany | 95 |
| 5 | Canada | 74 |
| 6 | Hong Kong | 70 |
| 7 | United Kingdom | 52 |
| 8 | Australia | 49 |
| 9 | South Korea | 43 |
| 10 | Italy | 31 |

*Keywords with the most ASE papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 319 |
| 2 | static analysis | 67 |
| 3 | code generation | 54 |
| 4 | software testing | 42 |
| 5 | vulnerability detection | 39 |
| 6 | bug detection | 35 |
| 7 | LLM agents | 33 |
| 8 | retrieval-augmented generation | 29 |
| 9 | test case generation | 28 |
| 10 | multi-agent systems | 27 |

## Most-cited papers

*Most-cited ASE papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 89 | [On the Evaluation of Large Language Models in Unit Test Generation](https://doi.org/10.1145/3691620.3695529) | 2024-10-18 | Lin Yang, Chen Yang, Shutao Gao et al. |
| 2 | 74 | [HITS: High-coverage LLM-based Unit Test Generation via Method Slicing](https://doi.org/10.1145/3691620.3695501) | 2024-10-18 | Zejun Wang, Kaibo Liu, Ge Li et al. |
| 3 | 40 | [Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?](https://doi.org/10.1145/3691620.3695065) | 2024-10-18 | Luan Pham, Huong Ha, Hongyu Zhang |
| 4 | 38 | [Semantic Sleuth: Identifying Ponzi Contracts via Large Language Models](https://doi.org/10.1145/3691620.3695055) | 2024-10-18 | Cong Wu, Jing Chen, Ziwei Wang et al. |
| 5 | 35 | [LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models](https://doi.org/10.1145/3691620.3695520) | 2024-10-18 | Shuncheng Tang, Zhenya Zhang, Jixiang Zhou et al. |
| 6 | 33 | [Test-Driven Development and LLM-based Code Generation](https://doi.org/10.1145/3691620.3695527) | 2024-10-18 | Noble Saji Mathews, Meiyappan Nagappan |
| 7 | 32 | [iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring](https://doi.org/10.1145/3691620.3695508) | 2024-10-18 | Di Wu, Fangwen Mu, Lin Shi et al. |
| 8 | 26 | [SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving Testing](https://doi.org/10.1145/3691620.3695037) | 2024-10-18 | An Guo, Yuan Hua Zhou, Haoxiang Tian et al. |
| 9 | 24 | [LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invariant Inference](https://doi.org/10.1145/3691620.3695014) | 2024-10-18 | Guangyuan Wu, Wan-Quan Cao, Yuan Yao et al. |
| 10 | 21 | [ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts](https://doi.org/10.1145/3691620.3695349) | 2024-10-18 | Che Wang, Jiashuo Zhang, Jianbo Gao et al. |

## Cite this page

Scholar4AI. (2026). ASE: Automated Software Engineering Conference — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/ase

```bibtex
@misc{scholar4ai-proceedings-ase,
  author = {{Scholar4AI}},
  title = {ASE: Automated Software Engineering Conference — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/ase}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).