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Synthesizing Conjunctive Queries for Code Search

Authors Chengpeng Wang , Peisen Yao , Wensheng Tang , Gang Fan , Charles Zhang

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Author Details

Chengpeng Wang
  • The Hong Kong University of Science and Technology, China
Peisen Yao
  • Zhejiang University, Hangzhou, China
Wensheng Tang
  • The Hong Kong University of Science and Technology, China
Gang Fan
  • Ant Group, Shenzhen, China
Charles Zhang
  • The Hong Kong University of Science and Technology, China

Funding

The authors are supported by the RGC16206517, ITS/440/18FP and PRP/004/21FX grants from the Hong Kong Research Grant Council and the Innovation and Technology Commission, Ant Group, and the donations from Microsoft and Huawei.

Acknowledgements

We thank the anonymous reviewers, Xiao Xiao, and Xiaoheng Xie for their helpful comments. Peisen Yao is the corresponding author.

Cite AsGet BibTex

Chengpeng Wang, Peisen Yao, Wensheng Tang, Gang Fan, and Charles Zhang. Synthesizing Conjunctive Queries for Code Search. In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 36:1-36:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ECOOP.2023.36

Abstract

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.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Automatic programming
  • Human-centered computing → User interface programming
Keywords
  • Query Synthesis
  • Multi-modal Program Synthesis
  • Code Search

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