Demo Data from EDBT 2026 / reviewers in the wild / expert
Xihan Zhou
dblp:397/3728
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
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 |
Program analysis · 83% Compilers and program optimization · 17% | |
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security › smart contract analysis
smart contract defect detection |
0.9 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Blockchain and cryptocurrency security
smart contract security |
0.9 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Program analysis
smart contract analysis |
0.9 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Program analysis
static analysis |
0.9 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Program analysis › static analysis
taint analysis |
0.9 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Compilers and program optimization
intermediate representation |
0.3 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Compilers and program optimization › intermediate representation
static single assignment form |
0.3 | 1 | 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract Defects · ICSE 2025 |
Methods — techniques the papers use, named apart from their topics
taint analysis · 1.7static analysis · 1.7data dependency analysis · 1.7control flow graph · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Enhancing the Open Network: Definition and Automated Detection of Smart Contract DefectsabstractThe Open Network (TON), designed to support Telegram's extensive user base of hundreds of millions, has garnered considerable attention since its launch in 2022. FunC is the most popular programming language for writing smart contracts on TON. It is distinguished by a unique syntax compared to other smart contract languages. Despite growing interest, research on the practical defects of TON smart contracts is still in its early stages. In this paper, we summarize eight smart contract defects identified from TON's official blogs and audit reports, each with detailed definitions and code examples. Furthermore, we propose a static analysis framework called TONScanner to facilitate the detection of these defects. Specifically, TONScanner reuses FunC compiler's frontend code to transform the FunC source code into FunC intermediate representation (IR) in the form of a directed acyclic graph (DAG). Based on this IR, TONScanner constructs a control flow graph (CFG), then transforms it into a static single assignment (SSA) form to simplify further analysis. TONScanner also integrates Data Dependency, Call Graph, Taint Analysis, and Cell Construct, which are specifically tailored for TON blockchain's unique data structures. These components finally facilitate the identification of the eight defects. We evaluate the effectiveness of TONScanner by applying it to 1,640 smart contracts and find a total of 14,995 defects. Through random sampling and manual labeling, we find that TONScanner achieves an overall precision of 97.49%. The results reveal that current TON contracts contain numerous defects, indicating that developers are prone to making errors. TONScanner has proven its ability to accurately identify these defects, thereby aiding in their correction. Jiachi Chen, Ting Chen 0002, Beibei Li 0002, Zhangyan Lin, Xihan Zhou |
ICSE | 9 |