Demo Data from EDBT 2026 / reviewers in the wild / expert
HaoYue Kang
dblp:415/5908
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge graph reasoning |
0.9 | 1 | 2025 | APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge Graph · IJCAI 2025 |
Software maintenance and evolution › software reengineering › software modernization › software migration
API migration |
0.9 | 1 | 2025 | APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge Graph · IJCAI 2025 |
Software maintenance and evolution › software evolution
library evolution |
0.9 | 1 | 2025 | APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge Graph · IJCAI 2025 |
Software maintenance and evolution
software ecosystems |
0.9 | 1 | 2025 | APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge Graph · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.7knowledge graph reasoning · 1.7chain exploration · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge GraphabstractAPI migration is essential for software maintenance due to the rapid evolution of third-party libraries where API elements may change continuously through updates. There are two main challenges for API migration at the project level, especially across multiple versions: 1) lack of specific library evolution knowledge across multi-version; 2) difficulty in identifying the chain of changes at the project level. This paper proposes a project-level cross-multi-version API migration framework APIMig. We first construct an API evolution knowledge graph (KG) to capture changes between adjacent library versions and then derive coherent cross-version API evolution knowledge by KG reasoning. Second, we design a chain exploration algorithm to track the chain of changes and aggregate the affected code segments. Finally, a large language model is employed in completing API migration by providing the API evolution knowledge and the chain of changes. We construct an evolution KG for the Lucene library from version 4.0.0 to 10.1.0 and evaluate our approach through project migration pairs that depend on different major versions. Our framework shows improvements over the baseline in migrating projects across 7 major versions, achieving average increases of 16.52% in CodeBLEU scores and 28.49% in VCEU scores in GPT-4o. Li Kuang, Qi Xie 0010, Haiyang Yang, HaoYue Kang, Yingjie Xia |
IJCAI | 6 |