EDBT 2026 Demo / reviewers in the wild / expert
Ducson Nguyen
dblp:167/0198
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
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.
| Network and information security
1 paper |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › memory safety › memory error detection
buffer overflow detection |
0.2 | 1 | 2015 | Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015 |
Systems and software security
memory safety |
0.2 | 1 | 2015 | Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015 |
Program analysis
static analysis |
0.2 | 1 | 2015 | Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015 |
Methods — techniques the papers use, named apart from their topics
heuristic static analysis · 0.4DWARF debugging information · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Data-Delineation in Software Binaries and its Application to Buffer-Overrun DiscoveryabstractDetecting memory-safety violations in binaries is complicated by the lack of knowledge of the intended data layout, i.e., the locations and sizes of objects. We present lightweight, static, heuristic analyses for recovering the intended layout of data in a stripped binary. Comparison against DWARF debugging information shows high precision and recall rates for inferring source-level object boundaries. On a collection of benchmarks, our analysis eliminates a third to a half of incorrect object boundaries identified by an IDA Pro-inspired heuristic, while retaining nearly all valid object boundaries. In addition to measuring their accuracy directly, we evaluate the effect of using the recovered data for improving the precision of static buffer-overrun detection in the defect-detection tool CodeSonar/x86. We demonstrate that CodeSonar's false-positive rate drops by about 80% across our internal evaluation suite for the tool, while our approximation of CodeSonar's recall only degrades about 25%. Denis Gopan, Evan Driscoll, Ducson Nguyen, Dimitri Naydich, Alexey Loginov, David Melski |
ICSE (1) | 3 |