GitHub introduced Issue Report Templates to streamline issue management and provide essential information for developers. However, many projects still face a high volume of non-template issue reports, which are often difficult to under-stand and challenging for users to properly complete using the templates. This paper aimed to explore better solutions through an empirical analysis of Transformer models for automatically templating issue reports. We examined the impact of different architectures and hyperparameters on the performance of Trans-former models in handling automatic templating tasks via ex-tensive experiments. Additionally, we evaluated the performance of GPT-3.5 and Claude-3 on this task using various prompts. Our results indicate that models based on the Transformer architecture outperform baseline models. Specifically, the BERT model achieved the best performance with an accuracy of 0.863 and an F1 score of 0.857. We reported on how the performance of different models is influenced by variations in hyperparameters. Moreover, generative large language models like GPT-3.5 and Claude-3 may not be suitable for direct application in this automatic templating problem. Our findings highlight the efficacy of Transformer models for automatic templating tasks and encourage researchers to investigate more advanced approaches for understanding and analyzing issue reports.

GitHub introduced Issue Report Templates to safe-guard the quality of bug reports, but its related research is scarce and data is limited. In this study, the categories, distribution and changes in IRT and the relationship with project characteristics were analyzed for 1,084,300 progects. The results of the study found that BugReport was the most numerous IRT. IRTs are mainly distributed in languages such as Python and TypeScript. The number of IRTs has increased over time but the complexity has remained constant. Projects that use IRTs have larger project characteristic values. In addition, we found that the adoption of IRT was associated with increased project productivity leading to more successful projects.

In collaborative software development, bots have become increasingly prevalent, making effective bot recommendation a key factor in enhancing development efficiency. This study aims to explore the application and efficacy of deep learning models in bot recommendation. Focusing on the CodeBERT model, we conduct a comprehensive evaluation through comparison with baseline models, parameter tuning (including batch size and learning rate), and the incorporation of language data. Our findings demonstrate that under specific conditions, the CodeBERT model exhibits superior performance in bot recommendation tasks, with parameter adjustments and the inclusion of language data significantly impacting the model's effectiveness. These insights offer new perspectives and strategies for the effective recommendation of bots in open-source software platforms.