PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929449540255744 |
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| author | He, Junda Xu, Bowen Yang, Zhou Han, DongGyun Yang, Chengran Liu, Jiakun Zhao, Zhipeng Lo, David |
| author_facet | He, Junda Xu, Bowen Yang, Zhou Han, DongGyun Yang, Chengran Liu, Jiakun Zhao, Zhipeng Lo, David |
| contents | 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% |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models He, Junda Xu, Bowen Yang, Zhou Han, DongGyun Yang, Chengran Liu, Jiakun Zhao, Zhipeng Lo, David Software Engineering 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% |
| title | PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2408.02311 |