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 26–50 of 125 papers

TitleStatusHype
Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code SearchCode1
Multimodal Representation for Neural Code SearchCode1
CoSQA: 20,000+ Web Queries for Code Search and Question AnsweringCode1
deGraphCS: Embedding Variable-based Flow Graph for Neural Code SearchCode1
DOBF: A Deobfuscation Pre-Training Objective for Programming LanguagesCode1
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and GenerationCode1
PalmTree: Learning an Assembly Language Model for Instruction EmbeddingCode1
Search4Code: Code Search Intent Classification Using Weak SupervisionCode1
Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language IntentCode1
Faster Person Re-IdentificationCode1
funcGNN: A Graph Neural Network Approach to Program SimilarityCode1
A Toolkit for Generating Code Knowledge GraphsCode1
CodeSearchNet Challenge: Evaluating the State of Semantic Code SearchCode1
MGS3: A Multi-Granularity Self-Supervised Code Search Framework—0
DeepRTL2: A Versatile Model for RTL-Related Tasks—0
LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models—0
Knowledge Graph Based Repository-Level Code Generation—0
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks—0
Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code—0
A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection—0
OASIS: Order-Augmented Strategy for Improved Code Search—0
LoRACode: LoRA Adapters for Code Embeddings—0
MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings—0
Beyond Natural Language Perplexity: Detecting Dead Code Poisoning in Code Generation Datasets—0
URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in 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
3CodeT5+ 770MOverall77.4—Unverified
4GraphCodeBERTOverall77.4—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