| --- |
| license: mit |
| tags: |
| - TensorFlow |
| - BERT |
| - Transformer |
| - Classification |
| - Regression |
| --- |
| # Code Qualiy Evaluation Dataset |
| 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. |
|
|
| ## Contents |
| This repository contains the following: |
| - Fine-tuned Model |
| - Dataset (https://github.com/tisage/codeQuality) |
| - License |
|
|
| ## Model Info |
| There are three BERT models, each fine-tuned on a dataset of 70K Python 3 solutions submitted by users for problems #1 through #100 on LeetCode: |
| - `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. |
| - `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. |
| - `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. |
| - `bert_lc100_regression_v2`: similar to `bert_lc100_regression` model, the correctness score is calculated using more restricted rule `==` instead of similarity. |
|
|
| ## Model Usage |
| **Installation** |
| First, ensure you have the latest version of the tf-models-official package. You can install it using the following command: |
| ``` |
| pip install -q tf-models-official |
| ``` |
|
|
| **Loading the Model** |
| To utilize the bert_lc100_regression model within TensorFlow, follow these steps: |
| ``` |
| import tensorflow as tf |
| import tensorflow_text as text |
| model = tf.keras.models.load_model('saved_model/bert_lc100_regression/', compile=False) |
| ``` |
|
|
| **Making Predictions** |
| To assess the quality of code, given that `X_test` contains a list of code strings, use the model to predict as follows: |
| ``` |
| y_pred = model.predict(X_test) |
| ``` |
|
|
| ## Reference |
| If you found the dataset useful in your research or applications, please cite using the following BibTeX: |
| ``` |
| @INPROCEEDINGS{10386406, |
| author={Wang, Tianyu and Chen, Zhixiong}, |
| booktitle={2023 IEEE International Conference on Big Data (BigData)}, |
| title={Analyzing Code Text Strings for Code Evaluation}, |
| year={2023}, |
| volume={}, |
| number={}, |
| pages={5619-5628}, |
| keywords={Measurement;Deep learning;Codes;Bidirectional control;Organizations;Transformers;Software;code assessment;code annotation;deep learning;nature language processing;software assurance;code security}, |
| doi={10.1109/BigData59044.2023.10386406} |
| } |
| ``` |
