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

• URL: http://arxiv.org/abs/2310.13714v1
• Date: Sat, 14 Oct 2023 10:47:10 GMT
• Title: A study of the impact of generative AI-based data augmentation on software metadata classification
• Authors: Tripti Kumari, Chakali Sai Charan and Ayan Das
• Abstract summary: 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. In the official assessment, our system achieves a 4% increase in F1-score from baseline and the quality of generated data.
• Score: 1.1356542363919058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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