2026-08-08T07:39:49Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18229 2024-03-06T11:01:26Z ddc:004 open_access

Synthesizing Conjunctive Queries for Code Search Wang, Chengpeng Yao, Peisen Tang, Wensheng Fan, Gang Zhang, Charles Query Synthesis Multi-modal Program Synthesis Code Search This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Chengpeng Wang and Peisen Yao and Wensheng Tang and Gang Fan and Charles Zhang 2023 Is Part Of LIPIcs, Volume 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECOOP.2023.36 urn:nbn:de:0030-drops-182294 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2023.36 eng https://creativecommons.org/licenses/by/4.0/legalcode