https://github.com/reddy-lab-code-research/xlcost
공식 코드XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
Papers with Code 아카이브
Code Search
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/reinforest-team/reinforest
공식 코드REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models
https://github.com/salesforce/codet5
공식 코드CodeT5+: Open Code Large Language Models for Code Understanding and Generation
https://github.com/shangqing-liu/graphsearchnet
공식 코드GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
https://github.com/shushanarakelyan/modular_code_search
공식 코드https://github.com/squareslab/melt
공식 코드MELT: Mining Effective Lightweight Transformations from Pull Requests
https://github.com/squareslab/melt_action
공식 코드MELT: Mining Effective Lightweight Transformations from Pull Requests
https://github.com/uh-serg/ci-memorization
공식 코드Memorization and Generalization in Neural Code Intelligence Models
https://github.com/v587su/nlqf
공식 코드On the Importance of Building High-quality Training Datasets for Neural Code Search
https://github.com/vinayak1/gcbs
공식 코드Global Contrastive Batch Sampling via Optimization on Sample Permutations
https://github.com/wala/graph4code
공식 코드https://github.com/wangdeze18/DACL
공식 코드Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
https://github.com/wangdeze18/multilingual-adapter-for-se
공식 코드One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
https://github.com/wangguanan/light-reid
공식 코드https://github.com/wssun/badcode
공식 코드https://github.com/wssun/trancs
공식 코드https://github.com/ynklab/xcodesearchnet
공식 코드Constructing Multilingual Code Search Dataset Using Neural Machine Translation
https://github.com/zeberhart/zacq
공식 코드Generating Clarifying Questions for Query Refinement in Source Code Search
https://github.com/zeyugao/virtualcompiler
공식 코드ViC: Virtual Compiler Is All You Need For Assembly Code Search
https://github.com/zju-ctag/codebridge
공식 코드https://github.com/Avmb/semantic_neq_game
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/JasonZhu-WHU/CoderAssistant
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/NEUIR/CONAN
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
https://github.com/UmarFarooqui/MethodNamePrediction
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/UmarFarooqui/Springboard
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/amazon-science/contraclm
https://github.com/aminatadjer/test
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/aorwall/swe-bench-docker
https://github.com/autocoderoversg/auto-code-rover
https://github.com/celbree/reacc
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
https://github.com/code-desc/codesc
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/deeplearnxmu/unigencoder
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/deepsoftwareanalytics/omnigirl
https://github.com/facebookresearch/CodeGen
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset
https://github.com/graykode/commit-autosuggestions
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.