SOTAVerified

Code Search

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.

Source: When Deep Learning Met Code Search

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 125 papers

TitleStatusHype
Bag-of-Words Baselines for Semantic Code Search—0
BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?—0
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning—0
Beyond Natural Language Perplexity: Detecting Dead Code Poisoning in Code Generation Datasets—0
Cascaded Fast and Slow Models for Efficient Semantic Code Search—0
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search—0
Clone-Seeker: Effective Code Clone Search Using Annotations—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
CodeDSI: Differentiable Code Search—0
Code Representation Pre-training with Complements from Program Executions—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Code Search Debiasing:Improve Search Results beyond Overall Ranking Performance—0
Contrastive Learning of Natural Language and Code Representations for Semantic Code Search—0
Contrastive Prompt Learning-based Code Search based on Interaction Matrix—0
COSEA: Convolutional Code Search with Layer-wise Attention—0
Crowd Sourced Data Analysis: Mapping of Programming Concepts to Syntactical Patterns—0
CSSAM:Code Search via Attention Matching of Code Semantics and Structures—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
DeepRTL2: A Versatile Model for RTL-Related Tasks—0
Distilling Transformers for Neural Cross-Domain Search—0
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation—0
Energy-bounded Learning for Robust Models of Code—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark—0
Evaluation of Siamese Networks for Semantic Code Search—0
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All datasetsCodeSearchNetCoDescCodeXGLUE - AdvTestCodeSearchNet - RubyCodeXGLUE - WebQueryTestCoIR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1cpt-code MOverall93.5—Unverified
2cpt-code SOverall93.4—Unverified
3GraphCodeBERTOverall77.4—Unverified
4CodeT5+ 770MOverall77.4—Unverified
5CodeT5+ 220MOverall77.1—Unverified
6CodeBERTOverall76—Unverified
#ModelMetricClaimedVerifiedStatus
1Self-attentionTest MRR0.84—Unverified
2NBOWTest MRR0.81—Unverified
3RNNTest MRR0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1CodeT5+ 770MMRR44.7—Unverified
2CodeT5+ 220MMRR43.3—Unverified
3CodeBERTMRR27.19—Unverified
#ModelMetricClaimedVerifiedStatus
1Uni-SBTMRR0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1CodeBERTAccuracy47.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Voyage-code-002nDCG@1056.26—Unverified