Demo Data from VLDB 2026 / reviewers in the wild / expert
Neil Kulkarni
dblp:300/9089
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved
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 · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › model inference
grammar inference |
0.5 | 1 | 2021 | Learning Highly Recursive Input Grammars · ASE 2021 |
Methods — techniques the papers use, named apart from their topics
parse tree construction · 0.5oracle-based learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Learning Highly Recursive Input GrammarsabstractThis paper presents Arvada, an algorithm for learning context-free grammars from a set of positive examples and a Boolean-valued oracle. Arvada learns a context-free grammar by building parse trees from the positive examples. Starting from initially flat trees, Arvada builds structure to these trees with a key operation: it bubbles sequences of sibling nodes in the trees into a new node, adding a layer of indirection to the tree. Bubbling operations enable recursive generalization in the learned grammar. We evaluate Arvada against GLADE and find it achieves on average increases of 4.98× in recall and 3.13× in F1 score, while incurring only a 1.27× slowdown and requiring only 0.87× as many calls to the oracle. Arvada has a particularly marked improvement over GLADE on grammars with highly recursive structure, like those of programming languages. Neil Kulkarni, Caroline Lemieux, Koushik Sen |
ASE | 1 |