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--- |
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license: mit |
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tags: |
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- TensorFlow |
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- BERT |
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- Transformer |
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- Classification |
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- Regression |
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--- |
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# Code Qualiy Evaluation Dataset |
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Welcome to the repository for our research paper: T. Wang and Z. Chen, "Analyzing Code Text Strings for Code Evaluation," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 5619-5628, doi: 10.1109/BigData59044.2023.10386406. |
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## Contents |
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This repository contains the following: |
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- Fine-tuned Model |
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- Dataset (https://github.com/tisage/codeQuality) |
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- License |
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## Model Info |
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There are three BERT models, each fine-tuned on a dataset of 70,000 Python 3 solutions submitted by users for problems #1 through #100 on LeetCode: |
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- `bert_lc100_hp25`: This model classifies code based on the 25th percentile as its threshold. It is designed for identifying lower quartile code solutions in terms of quality or performance. |
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- `bert_lc100_hp50`: Operating with a median-based approach, this model uses the 50th percentile as its classification threshold. It is suitable for general assessments, providing a balanced view of code quality. |
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- `bert_lc100_regression`: Unlike the others, this is a regression model. It provides a nuanced prediction of the overall code quality score, offering a more detailed evaluation compared to the binary classification approach. |
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## Model Usage |
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**Installation** |
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First, ensure you have the latest version of the tf-models-official package. You can install it using the following command: |
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``` |
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pip install -q tf-models-official |
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``` |
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**Loading the Model** |
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To utilize the bert_lc100_regression model within TensorFlow, follow these steps: |
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``` |
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import tensorflow as tf |
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import tensorflow_text as text |
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model = tf.keras.models.load_model('saved_model/bert_lc100_regression/', compile=False) |
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``` |
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**Making Predictions** |
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To assess the quality of code, given that `X_test` contains a list of code strings, use the model to predict as follows: |
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``` |
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y_pred = model.predict(X_test) |
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``` |
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## Reference |
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If you found the dataset useful in your research or applications, please cite using the following BibTeX: |
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``` |
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@INPROCEEDINGS{10386406, |
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author={Wang, Tianyu and Chen, Zhixiong}, |
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booktitle={2023 IEEE International Conference on Big Data (BigData)}, |
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title={Analyzing Code Text Strings for Code Evaluation}, |
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year={2023}, |
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volume={}, |
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number={}, |
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pages={5619-5628}, |
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keywords={Measurement;Deep learning;Codes;Bidirectional control;Organizations;Transformers;Software;code assessment;code annotation;deep learning;nature language processing;software assurance;code security}, |
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doi={10.1109/BigData59044.2023.10386406} |
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} |
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``` |
