https://github.com/rigorous-software-engineering/nomos
공식 코드Automatically Testing Functional Properties of Code Translation Models
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Code translation is the process of converting code written in one programming language to another programming language while maintaining the same functionality. This process is also known as code conversion, source-to-source translation, or transpilation. Code translation is often performed when developers want to take advantage of new programming languages, improve code performance, or maintain legacy systems. Some common examples include translating code from Python to Java, or from JavaScript to TypeScript.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Automatically Testing Functional Properties of Code Translation Models
SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
NatGen: Generative pre-training by "Naturalizing" source code
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
Unified Pre-training for Program Understanding and Generation
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
The impact of lexical and grammatical processing on generating code from natural language
The impact of lexical and grammatical processing on generating code from natural language
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
NatGen: Generative pre-training by "Naturalizing" source code
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.