Synthesizing Conjunctive Queries for 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.
Thu 20 JulDisplayed time zone: Pacific Time (US & Canada) change
13:30 - 15:00 | ECOOP 5: SynthesisResearch Papers at Habib Classroom (Gates G01) Chair(s): Karine Even-Mendoza King’s College London | ||
13:30 15mTalk | Synthesis-Aided Crash Consistency for Storage Systems Research Papers Jacob Van Geffen Veridise Inc., James Bornholt University of Texas at Austin, Emina Torlak Amazon Web Services and University of Washington, Xi Wang University of Washington DOI | ||
13:45 15mTalk | Synthesizing Conjunctive Queries for Code Search Research Papers Chengpeng Wang Hong Kong University of Science and Technology, Peisen Yao Zhejing University, Wensheng Tang Hong Kong University of Science and Technology, Gang Fan Ant Group, Charles Zhang Hong Kong University of Science and Technology DOI | ||
14:00 15mTalk | Hoogle⋆: Constants and λ-abstractions in Petri-net-based Synthesis using Symbolic Execution Research Papers Henrique Botelho Guerra INESC-ID and IST, University of Lisbon, João F. Ferreira INESC-ID and IST, University of Lisbon, João Costa Seco NOVA-LINCS; Nova University of Lisbon DOI | ||
14:15 15mTalk | Building Code Transpilers for Domain-Specific Languages Using Program Synthesis Research Papers Sahil Bhatia University of California, Berkeley, Sumer Kohli UC Berkeley, Sanjit Seshia UC Berkeley, Alvin Cheung University of California at Berkeley DOI | ||
14:30 15mTalk | Do Machine Learning Models Produce TypeScript Types that Type Check? Research Papers DOI Media Attached File Attached | ||
14:45 15mTalk | Toward Tool-Independent Summaries for Symbolic Execution Research Papers Frederico Ramos Instituto Superior Técnico, Nuno Sabino Instituto Superior Técnico, Carnegie Mellon University, Pedro Adão IST-ULisboa and Instituto de Telecomunicações, David Naumann Stevens Institute of Technology, José Fragoso Santos INESC-ID/Instituto Superior Técnico, Portugal DOI |