Demo Data from VLDB 2026 / reviewers in the wild / expert
Matthew Noonan
dblp:177/9217
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author
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 |
Programming languages and type systems · 100% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Programming languages and type systems › type inference
polymorphic type inference |
0.2 | 1 | 2016 | Polymorphic type inference for machine code · PLDI 2016 |
Programming languages and type systems
type inference |
0.2 | 1 | 2016 | Polymorphic type inference for machine code · PLDI 2016 |
Systems and software security
reverse engineering |
0.1 | 1 | 2016 | Polymorphic type inference for machine code · PLDI 2016 |
Methods — techniques the papers use, named apart from their topics
subtyping · 0.5recursive types · 0.5points-to analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Polymorphic type inference for machine codeabstractFor many compiled languages, source-level types are erased very early in the compilation process. As a result, further compiler passes may convert type-safe source into type-unsafe machine code. Type-unsafe idioms in the original source and type-unsafe optimizations mean that type information in a stripped binary is essentially nonexistent. The problem of recovering high-level types by performing type inference over stripped machine code is called type reconstruction, and offers a useful capability in support of reverse engineering and decompilation. In this paper, we motivate and develop a novel type system and algorithm for machine-code type inference. The features of this type system were developed by surveying a wide collection of common source- and machine-code idioms, building a catalog of challenging cases for type reconstruction. We found that these idioms place a sophisticated set of requirements on the type system, inducing features such as recursively-constrained polymorphic types. Many of the features we identify are often seen only in expressive and powerful type systems used by high-level functional languages. Using these type-system features as a guideline, we have developed Retypd: a novel static type-inference algorithm for machine code that supports recursive types, polymorphism, and subtyping. Retypd yields more accurate inferred types than existing algorithms, while also enabling new capabilities such as reconstruction of pointer const annotations with 98% recall. Retypd can operate on weaker program representations than the current state of the art, removing the need for high-quality points-to information that may be impractical to compute. Matthew Noonan, Alexey Loginov, David R. Cok |
PLDI | 1 |