Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yichuan Li 0005
dblp:216/7478-5
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-9166-087XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software 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
1 paper |
Program analysis · 56% Compilers and program optimization · 44% | |
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security
smart contract analysis |
0.8 | 1 | 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts · Proc. ACM Program. Lang. 2024 |
Blockchain and cryptocurrency security › smart contract analysis
smart contract auditing |
0.8 | 1 | 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts · Proc. ACM Program. Lang. 2024 |
Compilers and program optimization
decompilation |
0.8 | 1 | 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts · Proc. ACM Program. Lang. 2024 |
Program analysis
static analysis |
0.8 | 1 | 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts · Proc. ACM Program. Lang. 2024 |
Program analysis › binary analysis
bytecode analysis |
0.2 | 1 | 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts · Proc. ACM Program. Lang. 2024 |
Methods — techniques the papers use, named apart from their topics
type inference · 1.5abstract interpretation · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart ContractsabstractSince funds or tokens in smart contracts are maintained through specific state variables, contract audit, an effective means for security assurance, particularly focuses on these variables and their related operations. However, the absence of publicly accessible source code for numerous contracts, with only bytecode exposed, hinders audit efforts. Recovering variables and their types from Solidity bytecode is thus a critical task in smart contract analysis and audit, yet this is a challenging task because the bytecode loses variable and type information, only with low-level data operated by stack manipulations and untyped memory/storage accesses. The state-of-the-art smart contract decompilers miss identifying many variables and incorrectly infer the types for many identified variables. To this end, we propose VarLifter , a lifter dedicated to the precise and efficient recovery of typed variables. VarLifter interprets every read or written field of a data region as at least one potential variable, and after discarding falsely identified variables, it progressively refines the variable types based on the variable behaviors in the form of operation sequences. We evaluate VarLifter on 34,832 real-world Solidity smart contracts. VarLifter attains a precision of 97.48% and a recall of 91.84% for typed variable recovery. Moreover, VarLifter finishes analyzing 77% of smart contracts in around 10 seconds per contract. If VarLifter is used to replace the variable recovery modules of the two state-of-the-art Solidity bytecode decompilers, 52.4%, and 74.6% more typed variables will be correctly recovered, respectively. The applications of VarLifter to contract decompilation, contract audit, and contract bytecode fuzzing illustrate that the recovered variable information improves many contract analysis tasks. Yichuan Li 0005, Wei Song 0003, Jeff Huang 0001 |
Proc. ACM Program. Lang. | 1 |