EDBT 2026 Demo / reviewers in the wild / expert
Chengxuan Zhang
dblp:365/3766
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0003-0186-9560ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 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 verification · 81% Program analysis · 19% | |
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% |
Topics — the 3 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 |
0.9 | 1 | 2025 | Automated Invariant Generation for Solidity Smart Contracts · IEEE Trans. Dependable Secur. Comput. 2025 |
Program verification
invariant generation |
0.9 | 1 | 2025 | Automated Invariant Generation for Solidity Smart Contracts · IEEE Trans. Dependable Secur. Comput. 2025 |
Program verification
static verification |
0.3 | 1 | 2025 | Automated Invariant Generation for Solidity Smart Contracts · IEEE Trans. Dependable Secur. Comput. 2025 |
Methods — techniques the papers use, named apart from their topics
static verification · 1.7dynamic inference · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Automated Invariant Generation for Solidity Smart ContractsabstractSmart contracts are computer programs running on blockchains to automate the transaction execution between users. The absence of contract specifications poses a real challenge to the correctness verification of smart contracts. Program invariants are properties that are always preserved throughout the execution, which characterize an important aspect of the program behaviors. In this paper, we propose a novel invariant generation framework, INVCON+, for Solidity smart contracts. INVCON+ extends the existing invariant detector, InvCon, to automatically produce verified contract invariants based on both dynamic inference and static verification. Unlike INVCON+, InvCon only produces likely invariants, which have a high probability to hold, yet are still not verified against the contract code. Particularly, INVCON+ is able to infer more expressive invariants that capture richer semantic relations of contract code. We evaluate INVCON+ on 361 ERC20 and 10 ERC721 real-world contracts, as well as common ERC20 vulnerability benchmarks. The experimental results indicate that INVCON+ efficiently produces high-quality invariant specifications, achieving a recall of 80%, which can be used to secure smart contracts from 17 types of common vulnerabilities. Ye Liu 0012, Chengxuan Zhang, Yi Li 0008 |
IEEE Trans. Dependable Secur. Comput. | 2 |