Demo Data from EDBT 2026 / reviewers in the wild / expert
Adam Seitz
dblp:248/1632
· 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
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 analysis · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
binary analysis |
0.9 | 1 | 2025 | Disassembly as Weighted Interval Scheduling with Learned Weights · SP 2025 |
Program analysis › binary analysis
disassembly |
0.9 | 1 | 2025 | Disassembly as Weighted Interval Scheduling with Learned Weights · SP 2025 |
Methods — techniques the papers use, named apart from their topics
weighted interval scheduling · 0.9learned weights · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Disassembly as Weighted Interval Scheduling with Learned WeightsabstractDisassembly is the first step of a variety of binary analysis and transformation techniques, such as reverse engineering, or binary rewriting. Recent disassembly approaches consist of three phases: an exploration phase, that overapproximates the binary's code; an analysis phase, that assigns weights to candidate instructions or basic blocks; and a conflict resolution phase, that downselects the final set of instructions. We present a disassembly algorithm that generalizes this pattern for a wide range of architectures, namely x86, x64, arm32, and aarch64. Our algorithm presents a novel conflict resolution method that reduces disassembly to weighted interval scheduling. Additionally, we present a weight assignment algorithm that allows us to learn optimal weights for the various disassembly heuristics in the analysis phase. Learned weights outperform manually tuned weights in most cases while reducing the number of necessary heuristics by 40% (by setting their weights to zero). Our implementation, built on top of Ddisasm, outperforms state-of-the-art disassemblers in several metrics and achieves the largest proportion of perfectly disassembled binaries by a wide margin in all evaluated datasets. Antonio Flores-Montoya, Junghee Lim, Adam Seitz, Akshay Sood, Edward Raff, James Holt |
SP | 3 |