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.

翻译：诸如GitHub和GitLab等平台引入问题报告模板（IRT），以实现更有效的问题管理并更好地契合开发者预期。然而，大多数仓库并未广泛采用这些模板，目前也尚无工具能辅助开发者生成模板。在本研究中，我们提出GIRT-Model，这是一种辅助语言模型，能够基于开发者关于模板结构与必需字段的指令自动生成IRT。我们构建了GIRT-Instruct数据集，其中包含指令与IRT的配对数据，IRT来源于GitHub仓库。我们使用GIRT-Instruct对T5-base模型进行指令微调，从而得到GIRT-Model。实验结果表明，GIRT-Model在IRT生成任务中优于通用语言模型（不同参数规模的T5与Flan-T5），其在ROUGE、BLEU、METEOR指标及人工评估上均取得显著更高的分数。此外，我们通过一项用户研究分析了GIRT-Model的有效性，参与者在研究中借助GIRT-Model撰写简短的IRT。结果显示，参与者认为GIRT-Model在模板自动生成方面具有实用性。我们期望通过推广GIRT-Model，能鼓励更多开发者在仓库中采纳IRT。我们已公开代码、数据集及模型，地址为https://github.com/ISE-Research/girt-model。