https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53723 Wed 10 Jan 2024 11:23:35 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50844 Wed 09 Aug 2023 09:17:33 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48757 pairs based on heuristic rules of SO, and constructs pairs (FRA stands out fragment) by using tutorial fragments and APIs. SO2RT then trains a semi-supervised transfer learning based detection model, which can transfer the API usage knowledge in SO QA pairs to tutorial fragments by utilizing the easy-to-extract relevance of pairs. Finally, relevant fragments of APIs can be discovered by consulting the trained model. In this way, the effort for labeling the relevance between tutorial fragments and APIs can be reduced. We evaluate SO2RT on Java and Android datasets containing 21, 008 pairs. Experimental results show that SO2RT improves the state-of-the-art approaches in terms of F-Measure on both datasets. Our user study further confirms the effectiveness of SO2RT in practice.]]> Wed 05 Apr 2023 13:49:13 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54704 Thu 28 Mar 2024 11:39:12 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46190 tutorial fragment. We consider the API explained by a tutorial fragment as an API tag. Generating API tags for a tutorial fragment can help understand, navigate, and retrieve the fragment. Existing approaches often do not perform well on API tag generation due to high manual effort and low accuracy. Like API tutorials, Stack Overflow (SO) is also an important learning resource that provides the explanations of APIs. Thus, SO posts also contain API tags. Besides, API tags of SO posts are abundant and can be extracted easily. In this paper, we propose a novel approach ATTACK (stands for A PI Tag for Tutorial frAgments using Crowd Knowledge), which can automatically generate API tags for tutorial fragments from SO posts. ATTACK first constructs ⟨Q&A pair,tag set⟩ pairs by extracting API tags of SO posts. Then, it trains a deep neural network with the attention mechanism to learn the semantic relatedness between Q&A pairs and the associated API tags, taking into consideration both textual descriptions and code in a Q&A pair. Finally, the trained model is used to generate API tags for tutorial fragments. We evaluate ATTACK on public Java and Android datasets containing 43,132 ⟨Q&A pair,tag set⟩ pairs. Experimental results show that ATTACK is effective and outperforms the state-of-the-art approaches in terms of F-Measure. Our user study further confirms the effectiveness of ATTACK in generating API tags for tutorial fragments. We also apply ATTACK to document linking and the results confirm the usefulness of API tags generated by ATTACK.]]> Mon 14 Nov 2022 11:02:06 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51385 Mon 04 Sep 2023 13:12:15 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48014 Fri 07 Jul 2023 13:26:33 AEST ]]>