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等平台引入了问题报告模板（Issue Report Templates，IRTs），以实现更高效的问题管理并更好地匹配开发者期望。然而，这些模板在大多数仓库中并未得到广泛应用，且目前尚无辅助开发者生成模板的工具。在本工作中，我们提出了GIRT-Model，一种基于开发者关于模板结构与必要字段的指令自动生成IRTs的辅助语言模型。我们构建了GIRT-Instruct数据集，其中包含由指令与IRTs组成的配对样本，IRTs来源于GitHub仓库。我们利用GIRT-Instruct对T5-base模型进行指令微调，以得到GIRT-Model。实验表明，GIRT-Model在IRTs生成任务上优于通用语言模型（不同参数规模的T5和Flan-T5），在ROUGE、BLEU、METEOR及人工评估中均取得了显著更高的分数。此外，我们通过一项用户研究分析了GIRT-Model的有效性——参与者使用GIRT-Model撰写简短IRTs。结果表明，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的应用，鼓励更多开发者在自己的仓库中采用IRTs。我们已在https://github.com/ISE-Research/girt-model上公开了代码、数据集及模型。