title: LLPut: Investigating Large Language Models for Bug Report-Based Input
    Generation
summary: 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.
paper_authors._id: 67e5fd806e73232cf08af3bb
paper_authors.name: Alif Al Hasan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-28T04:39:45.135Z
paper_authors._id: 659430138fec845e50a27558
paper_authors.avatarUrl: /avatars/f5de806f55c90ae303cd94af7c15005c.svg
paper_authors.fullname: Alif Al Hasan
paper_authors.type: user
paper_authors.user: alifalhasan
paper_authors._id: 67e5fd806e73232cf08af3bc
paper_authors.name: Subarna Saha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:19:03.409Z
paper_authors._id: 6664836f3038d313d1ac7867
paper_authors.avatarUrl: /avatars/30de3b29cf06b5481f100ace55a85f29.svg
paper_authors.fullname: Subarna Saha
paper_authors.type: user
paper_authors.user: Subarna10
paper_authors._id: 67e5fd806e73232cf08af3bd
paper_authors.name: Mia Mohammad Imran
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-28T01:38:09.589Z
paper_authors._id: 6331c3f618711776b468e9ec
paper_authors.avatarUrl: /avatars/af2c4bba031e474bf4fd2ea19e415aaf.svg
paper_authors.fullname: Mia Mohammad Imran
paper_authors.type: user
paper_authors.user: imranraad
paper_authors._id: 67e5fd806e73232cf08af3be
paper_authors.name: Tarannum Shaila Zaman
paper_title: LLPut: Investigating Large Language Models for Bug Report-Based Input
    Generation
paper_summary: 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.
paper_ai_summary: LLPut evaluates the performance of generative LLMs in extracting failure-inducing inputs from bug reports, providing insights into their capabilities and limitations.
paper_ai_keywords: Generative LLMs LLaMA Qwen Qwen-Coder Natural Language Processing NLP bug reports failure-inducing inputs automated input extraction automated bug diagnosis