Stack Overflow is a widely-used community Q&A website for programming-related queries.In such a platform, providing related questions as suggestions to the users can significantly enhance their search experience.Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited because the semantic and interaction features of the sentences may be lost.In this paper, we propose a novel method to predict the relatedness between questions based on semantic matching.We adopt the Interaction Feature Extractor to capture the interaction information and fuse it through a fusion mechanism to enhance the interaction between questions.Our experimental results demonstrate that our proposed method achieves stateof-the-art performance in terms of Precision, Recall, and F1score evaluation metrics, outperforming the baseline approaches.Furthermore, we show that our model also performs well in other semantic matching tasks in software fields, indicating its generalization ability and robustness.

As one of the most popular programming forums, Stack Overflow has helped many developers with massive high-quality questions and answers. Particularly, the related questions identified by developers can supply targeted knowledge to solve the programming problems. However, it is difficult to identify all relevant questions by developers from massive questions in Stack Overflow. Although some studies have raised methods for automatically identifying relatedness between questions, only a few of them provided related questions to new query. In addition, the existing methods can not extract the global information between query and candidate questions in a proper way. In this paper, we propose a novel method that recommends the related questions to developers' new queries based on the semantic matching. We introduce a novel integral fusion to improve the global information extraction and use the inter-attention to capture the local interactive information. Besides, we have pre-trained domain-specific word embeddings to enhance the processing of software engineering information. The experiment results show that our model achieves competitive performance in MRR, nDCG@5, and nDCG@10 metrics in the related questions retrieval on Stack Overflow,

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users.To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective.Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost.In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically.We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism.The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall, and F1-score evaluation metrics, achieving state-of-the-art performance in this task.Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost. In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically. We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism. The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall and [Formula: see text]-score evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.