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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JetBrains-Research/psiminer
공식 코드PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
https://github.com/basedrhys/obfuscated-code2vec
공식 코드Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
https://github.com/iamfaith/transformcode
공식 코드TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation
https://github.com/mdrafiqulrabin/CI-Memorization
공식 코드Memorization and Generalization in Neural Code Intelligence Models
https://github.com/mdrafiqulrabin/SIVAND
공식 코드Understanding Neural Code Intelligence Through Program Simplification
https://github.com/mdrafiqulrabin/ci-dd-perses
공식 코드Extracting Label-specific Key Input Features for Neural Code Intelligence Models
https://github.com/mdrafiqulrabin/handcrafted-embeddings
공식 코드https://github.com/mdrafiqulrabin/rm-dd-perses
공식 코드Extracting Label-specific Key Input Features for Neural Code Intelligence Models
https://github.com/mdrafiqulrabin/tnpa-framework
공식 코드https://github.com/mdrafiqulrabin/tnpa-generalizability
공식 코드Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations
https://github.com/parasj/contracode
공식 코드https://github.com/rmusab/wrd-score
공식 코드WRDScore: New Metric for Evaluation of Natural Language Generation Models
https://github.com/sleepyhead01/vulnurable-code-entities-r-analysis
공식 코드https://github.com/uh-serg/ci-memorization
공식 코드Memorization and Generalization in Neural Code Intelligence Models
https://github.com/ciselab/lampion
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
https://github.com/zetang94/ase2023_knm-lm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.