  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.
