Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yunlan Du
dblp:231/1907
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 1 first-authorSoftware 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 |
Program analysis · 67% Debugging and program repair · 33% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Debugging and program repair
fault localization |
0.5 | 1 | 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis · IEEE Trans. Software Eng. 2021 |
Program analysis
static analysis |
0.5 | 1 | 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis · IEEE Trans. Software Eng. 2021 |
Program analysis › static analysis › abstract interpretation
value set analysis |
0.5 | 1 | 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis · IEEE Trans. Software Eng. 2021 |
Systems and software security › vulnerability analysis
root cause analysis |
0.1 | 1 | 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis · IEEE Trans. Software Eng. 2021 |
Systems and software security
vulnerability analysis |
0.1 | 1 | 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis · IEEE Trans. Software Eng. 2021 |
Methods — techniques the papers use, named apart from their topics
value-set analysis · 1.0reverse execution · 1.0backward taint analysis · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | POMP++: Facilitating Postmortem Program Diagnosis with Value-Set AnalysisabstractWith the emergence of hardware-assisted processor tracing, execution traces can be logged with lower runtime overhead and integrated into the core dump. In comparison with an ordinary core dump, such a new post-crash artifact provides software developers and security analysts with more clues to a program crash. However, existing works only rely on the resolved runtime information, which leads to the limitation in data flow recovery within long execution traces. In this work, we propose POMP++, an automated tool to facilitate the analysis of post-crash artifacts. More specifically, POMP++ introduces a reverse execution mechanism to construct the data flow that a program followed prior to its crash. Furthermore, POMP++ utilizes Value-set Analysis, which helps to verify memory alias relation, to improve the ability of data flow recovery. With the restored data flow, POMP++ then performs backward taint analysis and highlights program statements that actually contribute to the crash. We have implemented POMP++ for Linux system on x86-32 platform, and tested it against various crashes resulting from 31 distinct real-world security vulnerabilities. The evaluation shows that, our work can pinpoint the root causes in 29 cases, increase the number of recovered memory addresses by 12 percent and reduce the execution time by 60 percent compared with existing reverse execution. In short, POMP++ can accurately and efficiently pinpoint program statements that truly contribute to the crashes, making failure diagnosis significantly convenient. Dongliang Mu, Yunlan Du, Jianhao Xu, Jun Xu 0024, Xinyu Xing 0001, Bing Mao 0001, Peng Liu 0005 |
IEEE Trans. Software Eng. | 2 |
| 2020 | HART: Hardware-Assisted Kernel Module Tracing on Arm
Yunlan Du, Zhenyu Ning, Jun Xu 0024, Yueh-Hsun Lin, Fengwei Zhang, Xinyu Xing 0001, Bing Mao 0001 |
ESORICS (1) | 1 |
| 2018 | Mapping to Bits: Efficiently Detecting Type Confusion ErrorsabstractThe features of modularity and inheritance in C++ facilitate the developers' usage, but also give rise to the problem of type confusion. As an ancestor class may have a different data layout from its descendant class, a dangerous downcasting operation from the ancestor to its descendant can lead to a critical attack, such as control flow hijacking, out-of-bounds access to neighbor memory area, etc. As reported in CVE, such vulnerabilities have been found in various common-used software, including Google Chrome, Firefox and Adobe Flash Player, and have a trend of increase in recent years. The urgency of addressing type confusion problems quickens the pace of researchers coming to corresponding solutions. However, the existing works either handle the problem partially, or suffer from the high performance and memory overhead, especially to the large-scale projects. Chengbin Pang, Yunlan Du, Bing Mao 0001, Shanqing Guo |
ACSAC | 2 |