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arXiv · 2310.13714

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

Tripti Kumari·Chakali Sai Charan·Ayan Das
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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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Tripti Kumari, Chakali Sai Charan, Ayan Das. 2023-10-14. A study of the impact of generative AI-based data augmentation on software metadata classification. https://arxiv.org/abs/2310.13714

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