Jialin Ye

dblp:320/2390 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Blockchain and cryptocurrency security › smart contract security
vulnerability detection
0.912025
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
0.312025
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
0.312025
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
YearPublicationVenuePosition
2025 PATEN: Identifying Unpatched Third-Party APIs via Fine-Grained Patch-Enhanced AST-Level Signature
abstract
Using 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