Code Search
47 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.
Libraries
Use these libraries to find Code Search models and implementationsDatasets
Latest papers
Source Code Clone Detection Using Unsupervised Similarity Measures
Assessing similarity in source code has gained significant attention in recent years due to its importance in software engineering tasks such as clone detection and code search and recommendation.
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree transformation
The main reason for this is that encoding each code token would cause model parameter inflation, resulting in a lot of parameters storing information that we are not very concerned about.
Language Models are Universal Embedders
As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.
Rethinking Negative Pairs in Code Search
In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE.
MELT: Mining Effective Lightweight Transformations from Pull Requests
By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in \comby, a language for structural code search and replace.
Constructing Multilingual Code Search Dataset Using Neural Machine Translation
Code search is a task to find programming codes that semantically match the given natural language queries.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
Backdooring Neural Code Search
Neural code search models are hence behind many such engines.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks.
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code.