IEA: an answerer recommendation approach on stack overflow

Wang, Liting; Zhang, Li; Jiang, Jing

doi:10.1007/s11432-018-9848-2

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…For example, Wang et al [36] looked into the influence of crowd-sourcing revision 3 on question body quality. Wang et al [35] built a hybrid tag recommender based on convolutional neural networks and collaborative filtering. Recently, Gao et al [10] for the first time proposed an end-to-end approach to generate question titles from given code snippets.…”

Section: Introductionmentioning

confidence: 99%

Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information

Zhang¹,

Keung²,

Li³

et al. 2021

Preprint

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Context: Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are of low-quality and thus obtain less attention from potential answerers. Gao et al. proposed a LSTM-based model (i.e., BiLSTM-CC) to automatically generate question titles from the code snippets to improve the question quality. However, only using the code snippets in question body cannot provide sufficient information for title generation, and LSTMs cannot capture the long-range dependencies between tokens. Objective: We propose CCBERT, a deep learning based novel model to enhance the performance of question title generation by making full use of the bi-modal information of the entire question body. Methods: CCBERT follows the encoder-decoder paradigm, and uses CodeBERT to encode the question body into hidden representations, a stacked Transformer decoder to generate predicted tokens, and an additional copy attention layer to refine the output distribution. Both the encoder and decoder perform the multi-head self-attention operation to better capture the long-range dependencies. We build a dataset containing more than 120,000 high-quality questions filtered from the data officially published by Stack Overflow to verify the effectiveness of the CCBERT model. Results: CCBERT achieves a better performance on the dataset, and especially outperforms BiLSTM-CC and a multi-purpose pre-trained model (BART) by 14% and 4% on average, respectively. Experiments on both code-only and low-resource datasets also show the superiority of CCBERT with less performance degradation, which are 40% and 13.5% for BiLSTM-CC, while 24% and 5% for CCBERT, respectively. Conclusion: CCBERT is capable of automatically capturing the bi-modal semantic information from the entire question body and parsing the long-range dependencies to achieve better performance. Therefore, CCBERT is an effective approach for Stack Overflow question title generation.

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Section: Introductionmentioning

confidence: 99%