تقرير
PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models
| العنوان: | PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models |
|---|---|
| المؤلفون: | He, Junda, Xu, Bowen, Yang, Zhou, Han, DongGyun, Yang, Chengran, Liu, Jiakun, Zhao, Zhipeng, Lo, David |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Software Engineering |
| الوصف: | Stack Overflow is one of the most influential Software Question & Answer (SQA) websites, hosting millions of programming-related questions and answers. Tags play a critical role in efficiently organizing the contents in Stack Overflow and are vital to support a range of site operations, e.g., querying relevant content. Poorly selected tags often raise problems like tag ambiguity and tag explosion. Thus, a precise and accurate automated tag recommendation technique is demanded. Inspired by the recent success of pre-trained models (PTMs) in natural language processing (NLP), we present PTM4Tag+, a tag recommendation framework for Stack Overflow posts that utilizes PTMs in language modeling. PTM4Tag+ is implemented with a triplet architecture, which considers three key components of a post, i.e., Title, Description, and Code, with independent PTMs. We utilize a number of popular pre-trained models, including the BERT-based models (e.g., BERT, RoBERTa, CodeBERT, BERTOverflow, and ALBERT), and encoder-decoder models (e.g., PLBART, CoTexT, and CodeT5). Our results show that leveraging CodeT5 under the PTM4Tag+ framework achieves the best performance among the eight considered PTMs and outperforms the state-of-the-art Convolutional Neural Network-based approach by a substantial margin in terms of average P recision@k, Recall@k, and F1-score@k (k ranges from 1 to 5). Specifically, CodeT5 improves the performance of F1-score@1-5 by 8.8%, 12.4%, 15.3%, 16.4%, and 16.6%. Moreover, to address the concern with inference latency, we experiment PTM4Tag+ with smaller PTM models (i.e., DistilBERT, DistilRoBERTa, CodeBERT-small, and CodeT5-small). We find that although smaller PTMs cannot outperform larger PTMs, they still maintain over 93.96% of the performance on average, meanwhile shortening the mean inference time by more than 47.2% Comment: arXiv admin note: substantial text overlap with arXiv:2203.10965 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.02311 |
| رقم الأكسشن: | edsarx.2408.02311 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |