PLangRec

Deep-learning Model to Predict the Programming Language from a Single Line of Code

Here you can download the following elements used in the article PLangRec: Deep-learning Model to Predict the Programming Language from a Single Line of Code:

  • Source code used to gather the source code from GitHub, perform language verification, and extract preprocessed lines (i.e., Figure 1 of the article).
  • Keras models of the MLP and BRNN models with best performance (8 layers), and the Keras model for the MLP stacking ensemble meta-model.
  • Raw evaluation data obtained in the different evaluations presented in the article.
  • Source code and binaries of PLangRec: the web API, web application and Python desktop program, together with the source code used to build and train the MLP and BRNN models, and the MLP stacking ensemble meta-model.
  • All the samples misclassified by PLangRec in the test dataset, indicating the actual language and the predicted one.
  • The corpus with 8.5 million source code files (21 languages) used to create the dataset (113 GBs).
  • The balanced dataset with 434.18 million samples in 21 different languages (27 GBs). The dataset is a collection of serialized binary Python pickle files that could be retrieved with any standard Python 3 implementation. No further processing is required.

LICENSE

MIT License.

Copyright 2024 (C) Computational Reflection research group, University of Oviedo.

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