Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models

TL;DR

This paper compares classical bag of words and transformer-based models for classifying code comments as relevant or not, revealing that bag of words surprisingly outperformed transformers in this task.

Contribution

The study provides an empirical comparison of feature engineering and classification techniques, including fine-tuned transformer models, for comment relevance classification.

Findings

Bag of words model outperformed transformer models in this task.

Transformer models showed limited performance on the given corpus.

The paper discusses limitations and future directions for model improvement.

Abstract

The Forum for Information Retrieval (FIRE) started a shared task this year for classification of comments of different code segments. This is binary text classification task where the objective is to identify whether comments given for certain code segments are relevant or not. The BioNLP-IISERB group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in this task and submitted five runs for five different models. The paper presents the overview of the models and other significant findings on the training corpus. The methods involve different feature engineering schemes and text classification techniques. The performance of the classical bag of words model and transformer-based models were explored to identify significant features from the given training corpus. We have explored different classifiers viz., random forest, support vector machine and logistic regression using the bag of words model. Furthermore, the pre-trained transformer based models like BERT, RoBERT and ALBERT were also used by fine-tuning them on the given training corpus. The performance of different such models over the training corpus were reported and the best five models were implemented on the given test corpus. The empirical results show that the bag of words model outperforms the transformer based models, however, the performance of our runs are not reasonably well in both training and test corpus. This paper also addresses the limitations of the models and scope for further improvement.