Demo Data from EDBT 2026 / reviewers in the wild / expert
Igor Wodiany
dblp:248/3016
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-7682-5581ORCID · 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 · 67% Software maintenance and evolution · 33% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
dynamic analysis |
0.8 | 1 | 2024 | LeanBin: Harnessing Lifting and Recompilation to Debloat Binaries · ASE 2024 |
Program analysis › dynamic analysis › trace analysis
execution trace analysis |
0.8 | 1 | 2024 | LeanBin: Harnessing Lifting and Recompilation to Debloat Binaries · ASE 2024 |
Systems and software security
attack surface reduction |
0.2 | 1 | 2024 | LeanBin: Harnessing Lifting and Recompilation to Debloat Binaries · ASE 2024 |
Methods — techniques the papers use, named apart from their topics
static analysis · 1.5recompilation · 1.5dynamic analysis · 1.5binary lifting · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | LeanBin: Harnessing Lifting and Recompilation to Debloat BinariesabstractTo reduce the source of potential exploits, binary debloating or specialization tools are used to remove unnecessary code from binaries. This paper presents a new binary debloating and specialization tool, LeanBin, that harnesses lifting and recompilation, based on observed execution traces. The dynamically recorded execution traces capture the required subset of instructions and control flow of the application binary for a given set of inputs. This initial control flow is subsequently augmented using heuristic-free static analysis to avoid excessively restricting the input space. The further structuring of the control flow and translation of binary instructions into a subset of C enables a lightweight generation of the code that can be recompiled, obtaining LLVM IR and a new debloated binary. Unlike most debloating approaches, LeanBin enables both binary debloating of the application and shared libraries, while reusing the existing compiler infrastructure. Additionally, unlike existing binary lifters, it does not rely on potentially unsound heuristics used by static lifters, nor suffers from long execution times, a limitation of existing dynamic lifters. Instead, LeanBin combines both heuristic-free static and dynamic analysis. The run time of lifting and debloating SPEC CPU2006 INT benchmarks has a geomean of 1.78×, normalized to the native execution, and the debloated binary runs with a geomean overhead of 1.21×. The percentage of gadgets, compared to the original binary, has a geomean between 24.10% and 30.22%, depending on the debloating strategy; and the code size can be as low as 53.59%. For the SQLite use-case, LeanBin debloats a binary including its shared library and generates a debloated binary that runs up to 1.24× faster with 3.65% gadgets. Igor Wodiany, Antoniu Pop, Mikel Luján |
ASE | 1 |