Code Search
48 papers with code • 5 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
Latest papers with no code
Analyzing CodeBERT's Performance on Natural Language Code Search
Large language models such as CodeBERT perform very well on tasks such as natural language code search.
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures.
Energy-bounded Learning for Robust Models of Code
In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on.
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation
This paper presents EDAssistant, a JupyterLab extension that supports EDA with in-situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results.
Semantic Code Search for Smart Contracts
To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features.
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.
A New Search Paradigm for Natural Language Code Search
Code search can accelerate the efficiency of software development by finding code snippets for the given query.
Cascaded Fast and Slow Models for Efficient Semantic Code Search
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query.
A Systematic Review of Automated Query Reformulations in Source Code Search
Developers consult these requests and often choose a few keywords from them as an ad hoc query.
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.
