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Kangjing Huang

dblp:215/3663 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Program synthesis and code generation
deductive program synthesis
0.412020
Reconciling enumerative and deductive program synthesis · PLDI 2020
Program synthesis and code generation › search-based program synthesis
enumerative synthesis
0.412020
Reconciling enumerative and deductive program synthesis · PLDI 2020
Program synthesis and code generation
syntax-guided synthesis
0.412020
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
YearPublicationVenuePosition
2022 Bootstrapping Library-Based Synthesis
Kangjing Huang, Xiaokang Qiu
SAS1
2020 Reconciling enumerative and deductive program synthesis
abstract
Syntax-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
PLDI1