mlsanityData to 14 Oct 2026

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

arXiv 2310.13714 ↗First posted 14 Oct 2023cs.SE cs.AI cs.CLcited by 0

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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.

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.

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References · 5

  1. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  2. 2023Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithmscited by 15
  3. 2018Predicting Usefulness of Code Review Comments using Textual Features and Developer Experiencecited by 13
  4. 2020Radial basis function kernel optimization for Support Vector Machine classifierscited by 7
  5. 2019Analyzing Code Comments to Boost Program Comprehensioncited by 3

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  4. 2025Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
  5. 2024AICoderEval: Improving AI Domain Code Generation of Large Language Models
  6. 2023Better Language Models of Code through Self-Improvement
  7. 2023Exploring the intersection of Generative AI and Software Development
  8. 2026Measuring and Mitigating Bias in Code Generated by Large Language Models