metadata

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%)

Metrics

The model is evaluated using the following metrics:

Accuracy
Precision
Recall
F1 Score (micro and macro average)

References

NLBSE22 Dataset

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},
}

I hope this helps. Let me know if you have any other questions.