GIRT-Model: Automated Generation of Issue Report Templates
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.
Mon 15 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | Machine learning for Software EngineeringTechnical Papers at Grande Auditório Chair(s): Diego Costa Concordia University, Canada | ||
16:00 12mTalk | Whodunit: Classifying Code as Human Authored or GPT-4 Generated - A case study on CodeChef problems Technical Papers Oseremen Joy Idialu University of Waterloo, Noble Saji Mathews University of Waterloo, Canada, Rungroj Maipradit University of Waterloo, Joanne M. Atlee University of Waterloo, Mei Nagappan University of Waterloo DOI Pre-print | ||
16:12 12mTalk | GIRT-Model: Automated Generation of Issue Report Templates Technical Papers Nafiseh Nikehgbal Sharif University of Technology, Amir Hossein Kargaran LMU Munich, Abbas Heydarnoori Bowling Green State University DOI Pre-print | ||
16:24 12mTalk | MicroRec: Leveraging Large Language Models for Microservice Recommendation Technical Papers Ahmed Saeed Alsayed University of Wollongong, Hoa Khanh Dam University of Wollongong, Chau Nguyen University of Wollongong | ||
16:36 12mTalk | PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software Technical Papers Wenxin Jiang Purdue University, Jerin Yasmin Queen's University, Canada, Jason Jones Purdue University, Nicholas Synovic Loyola University Chicago, Jiashen Kuo Purdue University, Nathaniel Bielanski Purdue University, Yuan Tian Queen's University, Kingston, Ontario, George K. Thiruvathukal Loyola University Chicago and Argonne National Laboratory, James C. Davis Purdue University DOI Pre-print | ||
16:48 12mTalk | Data Augmentation for Supervised Code Translation Learning Technical Papers Binger Chen Technische Universität Berlin, Jacek golebiowski Amazon AWS, Ziawasch Abedjan Leibniz Universität Hannover | ||
17:00 12mTalk | On the Effectiveness of Machine Learning-based Call-Graph Pruning: An Empirical Study Technical Papers Amir Mir Delft University of Technology, Mehdi Keshani Delft University of Technology, Sebastian Proksch Delft University of Technology Pre-print | ||
17:12 12mTalk | Leveraging GPT-like LLMs to Automate Issue Labeling Technical Papers Giuseppe Colavito University of Bari, Italy, Filippo Lanubile University of Bari, Nicole Novielli University of Bari, Luigi Quaranta University of Bari, Italy Pre-print | ||