Code Search
33 papers with code • 6 benchmarks • 10 datasets
The goal of Code Search is to retrieve code fragments from a large code corpus that most closely match a developer’s intent, which is expressed in natural language.
Benchmarks
These leaderboards are used to track progress in Code Search
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Libraries
Use these libraries to find Code Search models and implementationsDatasets
CodeSearchNet
CodeXGLUE
StaQC
XLCoST
ISAdetect dataset
Most implemented papers
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
microsoft/CodeBERT
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Findings of the Association for Computational Linguistics 2020
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
When Deep Learning Met Code Search
Our evaluation shows that: 1. adding supervision to an existing unsupervised technique can improve performance, though not necessarily by much; 2. simple networks for supervision can be more effective that more sophisticated sequence-based networks for code search; 3. while it is common to use docstrings to carry out supervision, there is a sizeable gap between the effectiveness of docstrings and a more query-appropriate supervision corpus.
CoNCRA: A Convolutional Neural Network Code Retrieval Approach
mrezende/concra
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We propose a technique for semantic code search: A Convolutional Neural Network approach to code retrieval (CoNCRA).
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks.
Memorization and Generalization in Neural Code Intelligence Models
The goal of this paper is to evaluate and compare the extent of memorization and generalization in neural code intelligence models.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
microsoft/CodeBERT
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ACL 2022
Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.
Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval
Analyzing this dataset, we observe that code related searching often requires more effort (e. g., time, result clicks, and query modifications) than general non-code search, which indicates code search performance with a general search engine is less effective.
A Toolkit for Generating Code Knowledge Graphs
wala/graph4code
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We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
