  Platforms such as GitHub and GitLab introduce Issue Report Templates (IRTs)
to enable more effective issue management and better alignment with developer
expectations. However, these templates are not widely adopted in most
repositories, and there is currently no tool available to aid developers in
generating them. In this work, we introduce GIRT-Model, an assistant language
model that automatically generates IRTs based on the developer's instructions
regarding the structure and necessary fields. We create GIRT-Instruct, a
dataset comprising pairs of instructions and IRTs, with the IRTs sourced from
GitHub repositories. We use GIRT-Instruct to instruction-tune a T5-base model
to create the GIRT-Model. In our experiments, GIRT-Model outperforms general
language models (T5 and Flan-T5 with different parameter sizes) in IRT
generation by achieving significantly higher scores in ROUGE, BLEU, METEOR, and
human evaluation. Additionally, we analyze the effectiveness of GIRT-Model in a
user study in which participants wrote short IRTs with GIRT-Model. Our results
show that the participants find GIRT-Model useful in the automated generation
of templates. We hope that through the use of GIRT-Model, we can encourage more
developers to adopt IRTs in their repositories. We publicly release our code,
dataset, and model at https://github.com/ISE-Research/girt-model.
