---
license: gpl-3.0
language:
- en
metrics:
- f1
---

# Model Card

## Model Details

- Model Name: IssueReportClassifier-NLBSE22
- Base Model: RoBERTa
- Dataset: NLBSE22
- Model Type: Fine-tuned
- Model Version: 1.0
- Model Date: 2023-03-21

## Model Description

IssueReportClassifier-NLBSE22 is a RoBERTa model which is fine-tuned on the NLBSE22 dataset.
The model is trained to classify issue reports from GitHub into three categories: bug, enhancement, and question.
The model is trained on a dataset of labeled issue reports and is designed to predict the category of a new issue report based on its text content (title and body).

## Dataset


| Category   | Training Set | Test Set |
|------------|--------------|-------------|
| bug        | 361,239 (50%) | 40,152 (49.9%) |
| enhancement | 299,287 (41.4%) | 33,290 (41.3%) |
| question   | 62,373 (8.6%) | 7,076 (8.8%) |


## Data preprocessing
The data used for training was preprocessed with [ekphrasis](https://github.com/cbaziotis/ekphrasis), adding some regular expressions to remove code, images and URLs. 
Check out our [GitHub](https://github.com/collab-uniba/Issue-Report-Classification-Using-RoBERTa) code for more information about this.


## Metrics

The model is evaluated using the following metrics:

- Accuracy
- Precision
- Recall
- F1 Score (micro and macro average)

## References

- [NLBSE22 Dataset](https://nlbse2022.github.io/tools/)

## Cite our work

```
@inproceedings{Colavito-2022,
  title = {Issue Report Classification Using Pre-trained Language Models},
  booktitle = {2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)},
  author = {Colavito, Giuseppe and Lanubile, Filippo and Novielli, Nicole},
  year = {2022},
  month = may,
  pages = {29--32},
  doi = {10.1145/3528588.3528659},
  abstract = {This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).},
  keywords = {Issue classification, BERT, deep learning, labeling unstructured data,
software maintenance and evolution},
}
```