Demo Data from EDBT 2026 / reviewers in the wild / expert
Tinghan Li
dblp:393/9242
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0001-6490-6210ORCID · 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.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 87% Program analysis · 13% | |
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security › smart contract security
vulnerability detection |
0.8 | 1 | 2024 | How Does Code Optimization Impact Third-party Library Detection for Android Applications? · ASE 2024 |
Software maintenance and evolution › software ecosystems
dependencies and software ecosystems |
0.8 | 1 | 2024 | How Does Code Optimization Impact Third-party Library Detection for Android Applications? · ASE 2024 |
Software maintenance and evolution › software dependencies
third-party library detection |
0.8 | 1 | 2024 | How Does Code Optimization Impact Third-party Library Detection for Android Applications? · ASE 2024 |
Program analysis
static analysis |
0.2 | 1 | 2024 | How Does Code Optimization Impact Third-party Library Detection for Android Applications? · ASE 2024 |
Methods — techniques the papers use, named apart from their topics
obfuscation resistance · 1.5code optimization analysis · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | How Does Code Optimization Impact Third-party Library Detection for Android Applications?abstractAndroid applications (apps) widely use third-party libraries (TPLs) to reuse functionalities and simplify the development process. Unfortunately, these TPLs often suffer from vulnerabilities that attackers can exploit, leading to catastrophic consequences for app users. To mitigate this threat, researchers have developed tools to detect TPL versions in the app. If an app is found using a TPL vulnerable version, these tools will issue warnings. Although these tools claim to resist the effects of code obfuscation, our preliminary study indicates that code optimization is common during the app release process. A lack of consideration for the impact of code optimizations significantly reduces the effectiveness of existing tools. To fill this gap, this work systematically investigates how and to what extent different optimization strategies affect existing tools. Our findings have led to a new tool named LibHunter, designed to against major code optimization strategies (e.g., Inlining and CallSite Optimization) while also resisting code obfuscation and shrinking. Extensive evaluations on a dataset of apps with optimization, obfuscation, and shrinking enabled show LibHunter significantly outperforms existing tools. It achieves F1 value that surpass the best tools by 29.3% and 36.1% at the library and version levels, respectively. We also applied LibHunter to detect vulnerable TPLs in the top Google Play apps, which shows the scalability of our approach, as well as the potential of our approach to facilitate malware detection. Zifan Xie, Ming Wen 0001, Tinghan Li, Yiding Zhu, Qinsheng Hou, Hai Jin 0001 |
ASE | 3 |