A study of the impact of generative AI-based data augmentation on
  software metadata classification
Tripti Kumari Chakali Sai Charan Ayan Das
This paper presents the system submitted by the team from IIT(ISM) Dhanbad in
FIRE IRSE 2023 shared task 1 on the automatic usefulness prediction of
code-comment pairs as well as the impact of Large Language Model(LLM) generated
data on original base data towards an associated source code. We have developed
a framework where we train a machine learning-based model using the neural
contextual representations of the comments and their corresponding codes to
predict the usefulness of code-comments pair and performance analysis with
LLM-generated data with base data. In the official assessment, our system
achieves a 4% increase in F1-score from baseline and the quality of generated
data.