Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kangjing Huang
dblp:215/3663
· DBLP profile ↗
2ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0003-4553-2281ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 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 synthesis and code generation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program synthesis and code generation
deductive program synthesis |
0.4 | 1 | 2020 | Reconciling enumerative and deductive program synthesis · PLDI 2020 |
Program synthesis and code generation › search-based program synthesis
enumerative synthesis |
0.4 | 1 | 2020 | Reconciling enumerative and deductive program synthesis · PLDI 2020 |
Program synthesis and code generation
syntax-guided synthesis |
0.4 | 1 | 2020 | Reconciling enumerative and deductive program synthesis · PLDI 2020 |
Methods — techniques the papers use, named apart from their topics
symbolic deduction · 0.4enumerative search · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Bootstrapping Library-Based Synthesis
Kangjing Huang, Xiaokang Qiu |
SAS | 1 |
| 2020 | Reconciling enumerative and deductive program synthesisabstractSyntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability. Kangjing Huang, Xiaokang Qiu, Peiyuan Shen, Yanjun Wang 0010 |
PLDI | 1 |