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

arXiv: https://arxiv.org/abs/2310.13714 · HTML: https://mlsanity.com/paper/2310.13714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms](https://mlsanity.com/paper/2303.17650/) (2023)
- [Predicting Usefulness of Code Review Comments using Textual Features and Developer Experience](https://mlsanity.com/paper/1807.04485/) (2018)
- [Radial basis function kernel optimization for Support Vector Machine classifiers](https://mlsanity.com/paper/2007.08233/) (2020)
- [Analyzing Code Comments to Boost Program Comprehension](https://mlsanity.com/paper/1905.02050/) (2019)

## Abstract (arXiv)

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.