LLPut: Investigating Large Language Models for Bug Report-Based Input
  Generation
Authors: name: Alif Al Hasan, name: Subarna Saha, name: Mia Mohammad Imran, name: Tarannum Shaila Zaman

Failure-inducing inputs play a crucial role in diagnosing and analyzing
software bugs. Bug reports typically contain these inputs, which developers
extract to facilitate debugging. Since bug reports are written in natural
language, prior research has leveraged various Natural Language Processing
(NLP) techniques for automated input extraction. With the advent of Large
Language Models (LLMs), an important research question arises: how effectively
can generative LLMs extract failure-inducing inputs from bug reports? In this
paper, we propose LLPut, a technique to empirically evaluate the performance of
three open-source generative LLMs -- LLaMA, Qwen, and Qwen-Coder -- in
extracting relevant inputs from bug reports. We conduct an experimental
evaluation on a dataset of 206 bug reports to assess the accuracy and
effectiveness of these models. Our findings provide insights into the
capabilities and limitations of generative LLMs in automated bug diagnosis.