---
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}
}
```