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