Automatic Classification of Source Code Archives by Programming Language: A Deep Learning Approach
| Autor: | Julio Reyes, Diego Ramirez, Julio Paciello |
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| Rok vydání: | 2016 |
| Předmět: |
Source code
Computer science media_common.quotation_subject Computer programming 010501 environmental sciences computer.software_genre Machine learning 01 natural sciences Naive Bayes classifier 0502 economics and business 050207 economics Fifth-generation programming language 0105 earth and related environmental sciences media_common Artificial neural network Programming language business.industry Deep learning 05 social sciences ComputingMethodologies_PATTERNRECOGNITION Recurrent neural network High-level programming language Programming paradigm Artificial intelligence business computer Low-level programming language Natural language processing |
| Zdroj: | 2016 International Conference on Computational Science and Computational Intelligence (CSCI). |
| DOI: | 10.1109/csci.2016.0103 |
| Popis: | This paper proposes the use of a Deep Learning technique, the Long Short-Term Memory (LSTM) recurrent neural network, for the automatic classification of source code archives by programming language. Experiments show that this simple recurrent neural network architecture gives promising results in accuracy compared to the Naive Bayes classifier, currently used by Linguist, one of the most popular programming language classifiers. |
| Databáze: | OpenAIRE |
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