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Tuo Li 0005

dblp:150/1609-5 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Program analysis
static analysis
3.252024
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
1.322024
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
0.812024
LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules · USENIX Security Symposium 2024
Program analysis › static analysis
pointer analysis
0.812024
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
0.812024
LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules · USENIX Security Symposium 2024
Concurrent programming
deadlock detection
0.612022
DLOS: Effective Static Detection of Deadlocks in OS Kernels · USENIX ATC 2022
Systems and software security › operating system security
device driver security
0.512021
Static Detection of Unsafe DMA Accesses in Device Drivers · USENIX Security Symposium 2021
Systems and software security
operating system security
0.512021
Static Detection of Unsafe DMA Accesses in Device Drivers · USENIX Security Symposium 2021
Program analysis › static analysis
interprocedural analysis
0.212024
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
0.212024
SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis · ACM Trans. Comput. Syst. 2024
Operating systems
kernel
0.212022
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
YearPublicationVenuePosition
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 Symposium1
2024 SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis
abstract
The 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 bugs
abstract
Operating 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
ASPLOS1
2022 DLOS: Effective Static Detection of Deadlocks in OS Kernels
Jia-Ju Bai, Tuo Li 0005, Shi-Min Hu 0001
USENIX ATC2
2021 Static Detection of Unsafe DMA Accesses in Device Drivers
Jia-Ju Bai, Tuo Li 0005, Kangjie Lu, Shi-Min Hu 0001
USENIX Security Symposium2