EDBT 2026 Demo / reviewers in the wild / expert
Jialin Ye
dblp:320/2390
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0004-9812-4999ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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.
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security › smart contract security
vulnerability detection |
0.9 | 1 | 2025 | PATEN: Identifying Unpatched Third-Party APIs via Fine-Grained Patch-Enhanced AST-Level Signature · IEEE Trans. Software Eng. 2025 |
Software maintenance and evolution › software ecosystems
dependency management |
0.3 | 1 | 2025 | PATEN: Identifying Unpatched Third-Party APIs via Fine-Grained Patch-Enhanced AST-Level Signature · IEEE Trans. Software Eng. 2025 |
Software maintenance and evolution › software ecosystems
third-party libraries |
0.3 | 1 | 2025 | PATEN: Identifying Unpatched Third-Party APIs via Fine-Grained Patch-Enhanced AST-Level Signature · IEEE Trans. Software Eng. 2025 |
Methods — techniques the papers use, named apart from their topics
vulnerability trace refinement · 1.7abstract syntax tree difference extraction · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PATEN: Identifying Unpatched Third-Party APIs via Fine-Grained Patch-Enhanced AST-Level SignatureabstractUsing a third-party library (TPL) API that is still unpatched with respect to known vulnerabilities would introduce severe security threats, and thus it is important to detect unpatched API as early as possible. Existing vulnerability detection methods often fail to identify subtle differences between patched and vulnerable versions of code, leading to high rates of false positives and missed vulnerabilities. Addressing these limitations, we propose a novel approach that employs a fine-grained, patch-enhanced Abstract Syntax Tree (AST) level signature. This approach consists of two key steps: patch-induced AST difference extraction and vulnerability trace refinement. These steps enable the detailed analysis of structural changes due to patches and enhance the accuracy of vulnerability detection by focusing on the critical elements of code changes. Building on this methodology, we introduce PATEN, a tool designed to accurately detect unpatched TPL APIs. Our evaluation, conducted on a large dataset, demonstrates that PATEN significantly outperforms the state-of-the-art approaches. Specifically, PATEN identified 82 critical vulnerabilities across numerous open-source projects, demonstrating a substantial advancement in the field of unpatched TPL API detection and highlighting its practical implications for improving software security. Jialin Ye, Rongxin Wu |
IEEE Trans. Software Eng. | 2 |