Searching a Database of Source Codes Using Contextualized Code Search

Summary: Learns to rank database code that completes a partial program from surrounding context, without explicit queries. A reverse encoder enables scalable retrieval over millions or billions of snippets by reducing candidate scoring to convolutions of Gaussian distributions. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 12266
Venue: VLDB
Year: 2020
Pagerank: 5.227679e-05
Overall Rank: 9,744 | 33.15%
DOI: 10.14778/3401960.3401972

Incoming Non-self Citations Over Time

Authors

1. Rohan Mukherjee (Rice University)
2. Swarat Chaudhuri (Rice University)
3. Chris Jermaine (Rice University)

BibTeX Citation

@article{mukherjee_vldb20,
        title = {{Searching a Database of Source Codes Using Contextualized Code Search}},
        author = {Mukherjee, Rohan and Chaudhuri, Swarat and Jermaine, Chris},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {10},
        pages = {1765--1778},
        doi = {10.14778/3401960.3401972},
        url = {https://doi.org/10.14778/3401960.3401972},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,882	CatDB: Data-catalog-guided, LLM-based Generation of Data-centric ML Pipelines	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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