A study of the impact of generative AI-based data augmentation on software metadata classification

Tripti Kumari, Chakali Sai Charan, Ayan Das
10/14/2023
cs.SEcs.AIcs.CL

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Abstract

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.

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Cite this paper

@article{kumari2023study,
  title  = {A study of the impact of generative AI-based data augmentation on software metadata classification},
  author = {Tripti Kumari and Chakali Sai Charan and Ayan Das},
  year   = {2023},
  eprint = {2310.13714},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.13714v1}
}

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