Demo Data from EDBT 2026 / reviewers in the wild / expert
Leandro Facchinetti
dblp:183/2724
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 3 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 |
Program analysis · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › static analysis › interprocedural analysis
context-sensitive analysis |
0.4 | 1 | 2019 | Higher-order Demand-driven Program Analysis · ACM Trans. Program. Lang. Syst. 2019 |
Program analysis › data flow analysis
flow-sensitive analysis |
0.4 | 1 | 2019 | Higher-order Demand-driven Program Analysis · ACM Trans. Program. Lang. Syst. 2019 |
Methods — techniques the papers use, named apart from their topics
pushdown automaton · 0.4demand-driven lookup · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | A Set-Based Context Model for Program Analysis
Leandro Facchinetti, Zachary Palmer, Scott F. Smith 0001, Ke Wu 0015, Ayaka Yorihiro |
APLAS | 1 |
| 2019 | Higher-order Demand-driven Program AnalysisabstractDeveloping accurate and efficient program analyses for languages with higher-order functions is known to be difficult. Here we define a new higher-order program analysis, Demand-Driven Program Analysis (DDPA), which extends well-known demand-driven lookup techniques found in first-order program analyses to higher-order programs. This task presents several unique challenges to obtain good accuracy, including the need for a new method for demand-driven lookup of non-local variable values. DDPA is flow- and context-sensitive and provably polynomial-time. To efficiently implement DDPA, we develop a novel pushdown automaton metaprogramming framework, the Pushdown Reachability automaton. The analysis is formalized and proved sound, and an implementation is described. Leandro Facchinetti, Zachary Palmer, Scott F. Smith 0001 |
ACM Trans. Program. Lang. Syst. | 1 |
| 2017 | Relative Store Fragments for Singleton Abstraction
Leandro Facchinetti, Zachary Palmer, Scott F. Smith 0001 |
SAS | 1 |