---
title: "ICPC (A): papers and universities"
description: "ICPC (IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 167 of its papers from 2024–2026 (45 in 2024, 69 in 2025, 53 in 2026). By the countries of the authors' institutions, the most papers come from China (55), the United States (24) and Switzerland (23)."
url: https://scholar4ai.com/proceedings/icpc
markdown_url: https://scholar4ai.com/proceedings/icpc.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICPC (IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 167 of its papers from 2024–2026 (45 in 2024, 69 in… (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)
---

# ICPC: IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006) — papers and institutions, 2024–2026

Papers of ICPC, IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006) (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

- ICPC (IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 167 of its papers from 2024–2026 (45 in 2024, 69 in 2025, 53 in 2026).
- By the countries of the authors' institutions, the most papers come from China (55), the United States (24) and Switzerland (23).
- The institutions with the most papers are Università della Svizzera italiana (16), University of Sannio (7) and Chinese Academy of Sciences (7).
- Its most-cited paper of these years is “Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies” (2024), with 54 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 45 | 44 |
| 2025 | 69 | 59 |
| 2026 | 53 | 53 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S7407088527](https://openalex.org/S7407088527) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | Università della Svizzera italiana | 16 |
| 2 | University of Sannio | 7 |
| 3 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 7 |
| 4 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 6 |
| 5 | [Central South University](https://scholar4ai.com/institutions/central-south-university.md) | 5 |
| 6 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | 5 |
| 7 | William & Mary | 5 |
| 8 | Indian Institute of Technology Tirupati | 5 |
| 9 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 5 |
| 10 | Polytechnique Montréal | 5 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 55 |
| 2 | United States | 24 |
| 3 | Switzerland | 23 |
| 4 | Canada | 20 |
| 5 | Italy | 20 |
| 6 | Netherlands | 8 |
| 7 | India | 7 |
| 8 | Brazil | 5 |
| 9 | France | 5 |
| 10 | Germany | 4 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 53 |
| 2 | program comprehension | 25 |
| 3 | code generation | 11 |
| 4 | code summarization | 11 |
| 5 | prompt engineering | 11 |
| 6 | open source software | 9 |
| 7 | bug reports | 8 |
| 8 | code readability | 8 |
| 9 | static analysis | 8 |
| 10 | code comments | 7 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 54 | [Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies](https://doi.org/10.1145/3643916.3644408) | 2024-04-15 | Yilun Liu, Shimin Tao, Weibin Meng et al. |
| 2 | 45 | [Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks](https://doi.org/10.1145/3643916.3644416) | 2024-04-15 | Domenico Cotroneo, Cristina Improta, Pietro Liguori et al. |
| 3 | 38 | [How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study](https://doi.org/10.1145/3643916.3644412) | 2024-04-15 | Federica Pepe, Vittoria Nardone, Antonio Mastropaolo et al. |
| 4 | 31 | [Investigating the Efficacy of Large Language Models for Code Clone Detection](https://doi.org/10.1145/3643916.3645030) | 2024-04-15 | Mohamad Khajezade, Jie JW Wu, Fatemeh H. Fard et al. |
| 5 | 29 | [Generating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants](https://doi.org/10.1145/3643916.3644402) | 2024-04-15 | Vincenzo Corso, Leonardo Mariani, Daniela Micucci et al. |
| 6 | 21 | [Compositional API Recommendation for Library-Oriented Code Generation](https://doi.org/10.1145/3643916.3644403) | 2024-04-15 | Zexiong Ma, Shengnan An, Bing Xie et al. |
| 7 | 19 | [Knowledge-Aware Code Generation with Large Language Models](https://doi.org/10.1145/3643916.3644418) | 2024-04-15 | Tao Huang, Zhihong Sun, Zhi Gang Jin et al. |
| 8 | 19 | [Analyzing Prompt Influence on Automated Method Generation: An Empirical Study with Copilot](https://doi.org/10.1145/3643916.3644409) | 2024-04-15 | Ionut Daniel Fagadau, Leonardo Mariani, Daniela Micucci et al. |
| 9 | 16 | [Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization](https://doi.org/10.1145/3643916.3644434) | 2024-04-15 | Jiliang Li, Yifan Zhang, Zachary Karas et al. |
| 10 | 14 | [Reassessing Java Code Readability Models with a Human-Centered Approach](https://doi.org/10.1145/3643916.3644435) | 2024-04-15 | Agnia Sergeyuk, Olga Lvova, Sergey Titov et al. |

## Cite this page

Scholar4AI. (2026). ICPC: IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006) — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/icpc

```bibtex
@misc{scholar4ai-proceedings-icpc,
  author = {{Scholar4AI}},
  title = {ICPC: IEEE International Conference on Program Comprehension (previously IWPC, changed in 2006) — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/icpc}},
  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/).