Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tuo Li 0005
dblp:150/1609-5
· DBLP profile ↗
5ranked-venue papers
3as first author
5since 2021 · last 2024
0000-0002-2955-6788ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 2 first-author · 3 since 2021Security and privacy · 2 · 1 first-author · 2 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 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
5 papers |
Program analysis · 70% Operating systems · 16% Concurrent programming · 14% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 11 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
static analysis |
3.2 | 5 | 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024 LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules · USENIX Security Symposium 2024 DLOS: Effective Static Detection of Deadlocks in OS Kernels · USENIX ATC 2022 |
Program analysis › type-based analysis
typestate analysis |
1.3 | 2 | 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024 Path-sensitive and alias-aware typestate analysis for detecting OS bugs · ASPLOS 2022 |
Concurrent programming › concurrency bug detection › data race detection
kernel data-race detection |
0.8 | 1 | 2024 | LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules · USENIX Security Symposium 2024 |
Program analysis › static analysis
pointer analysis |
0.8 | 1 | 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024 |
Program analysis › concurrent program analysis
static race detection |
0.8 | 1 | 2024 | LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules · USENIX Security Symposium 2024 |
Concurrent programming
deadlock detection |
0.6 | 1 | 2022 | DLOS: Effective Static Detection of Deadlocks in OS Kernels · USENIX ATC 2022 |
Systems and software security › operating system security
device driver security |
0.5 | 1 | 2021 | Static Detection of Unsafe DMA Accesses in Device Drivers · USENIX Security Symposium 2021 |
Systems and software security
operating system security |
0.5 | 1 | 2021 | Static Detection of Unsafe DMA Accesses in Device Drivers · USENIX Security Symposium 2021 |
Program analysis › static analysis
interprocedural analysis |
0.2 | 1 | 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024 |
Program analysis › static analysis › interprocedural analysis
summary-based analysis |
0.2 | 1 | 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024 |
Operating systems
kernel |
0.2 | 1 | 2022 | DLOS: Effective Static Detection of Deadlocks in OS Kernels · USENIX ATC 2022 |
Methods — techniques the papers use, named apart from their topics
static analysis · 1.8path-feasibility validation · 0.8locking rule mining · 0.8alias-summary-based analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules
Tuo Li 0005, Jia-Ju Bai, Gui-Dong Han, Shi-Min Hu 0001 |
USENIX Security Symposium | 1 |
| 2024 | SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate AnalysisabstractThe operating system (OS) is the cornerstone for computer systems. It manages hardware and provides fundamental service for user-level applications. Thus, detecting bugs in OSes is important to improve the reliability of computer systems. Static typestate analysis is a common technique for detecting various types of bugs, but it is often inaccurate or unscalable for large-size OS code, due to imprecision of identifying alias relationships as well as high costs of typestate tracking, path-feasibility validation, and inter-procedural analysis. In this article, 1 we present SPATA, a novel summary-based, alias-aware, and path-sensitive typestate analysis framework to detect OS bugs. To identify precise alias relationships in the OS code, SPATA performs a path-based alias analysis based on control-flow paths and access paths. With these alias relationships, SPATA reduces the costs of typestate tracking and path-feasibility validation, to accelerate path-sensitive typestate analysis for accurate bug detection. Moreover, SPATA uses an alias-summary-based analysis to accelerate inter-procedural bug detection, without time-consuming alias analysis across functions. We have evaluated SPATA on the Linux kernel and three popular IoT OSes, and it finds 651 real bugs with a false-positive rate of 18%. Besides, our alias-summary-based analysis achieves a 6.7x speedup in bug detection compared to non-summary-based analysis. Tuo Li 0005, Jia-Ju Bai, Yulei Sui, Shi-Min Hu 0001 |
ACM Trans. Comput. Syst. | 1 |
| 2022 | Path-sensitive and alias-aware typestate analysis for detecting OS bugsabstractOperating system (OS) is the cornerstone for modern computer systems. It manages devices and provides fundamental service for user-level applications. Thus, detecting bugs in OSes is important to improve reliability and security of computer systems. Static typestate analysis is a common technique for detecting different types of bugs, but it is often inaccurate or unscalable for large-size OS code, due to imprecision of identifying alias relationships as well as high costs of typestate tracking and path-feasibility validation. Tuo Li 0005, Jia-Ju Bai, Yulei Sui, Shi-Min Hu 0001 |
ASPLOS | 1 |
| 2022 | DLOS: Effective Static Detection of Deadlocks in OS Kernels
Jia-Ju Bai, Tuo Li 0005, Shi-Min Hu 0001 |
USENIX ATC | 2 |
| 2021 | Static Detection of Unsafe DMA Accesses in Device Drivers
Jia-Ju Bai, Tuo Li 0005, Kangjie Lu, Shi-Min Hu 0001 |
USENIX Security Symposium | 2 |