GIRT-Model: Automated Generation of Issue Report Templates
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916118133735424 |
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| author | Nikeghbal, Nafiseh Kargaran, Amir Hossein Heydarnoori, Abbas |
| author_facet | Nikeghbal, Nafiseh Kargaran, Amir Hossein Heydarnoori, Abbas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GIRT-Model: Automated Generation of Issue Report Templates Nikeghbal, Nafiseh Kargaran, Amir Hossein Heydarnoori, Abbas Software Engineering Computation and Language 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. |
| title | GIRT-Model: Automated Generation of Issue Report Templates |
| topic | Software Engineering Computation and Language |
| url | https://arxiv.org/abs/2402.02632 |