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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Thu 24 Sep 2020 01:30 - 01:50 at Wombat - Human-computer interaction Chair(s): Zhiyuan Wan

Code context models consist of source code elements and their relations relevant to a task in a developer’s hand. Prior research showed that making code context models explicit in software tools can benefit software development practices, e.g., code navigation and searching. However, little focus has been put on how to proactively form code context models. In this paper, we explore the proactive formation of code context models based on the topological patterns of code elements from interaction histories for a project. Specifically, we first learn abstract topological patterns based on the stereotype roles of code elements, rather than on specific code elements; we then leverage the learned patterns to predict the code context models for a given task by graph pattern matching. To determine the effectiveness of this approach, we applied the approach to interaction histories stored for the Eclipse Mylyn open source project.We found that our approach achieves maximum F-measures of 0.67, 0.33 and 0.21 for 1-step, 2-step and 3-step predictions, respectively. The most similar approach to ours is Suade, which supports 1-step prediction only. In comparison to this existing work, our approach predicts code context models with significantly higher F-measure (0.57 over 0.23 on average). The results demonstrate the value of integrating historical and structural approaches to form more accurate code context models.

Thu 24 Sep

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01:10 - 02:10
Human-computer interactionResearch Papers / Tool Demonstrations at Wombat
Chair(s): Zhiyuan Wan Zhejiang University
Identifying and Describing Information Seeking Tasks
Research Papers
Chris Satterfield University of British Columbia, Thomas Fritz University of Zurich, Gail Murphy University of British Columbia
Predicting Code Context Models for Software Development Tasks
Research Papers
Zhiyuan Wan Zhejiang University, Gail Murphy University of British Columbia, Xin Xia Monash University
Edge4Real: A Cost-Effective Edge Computing based Human Behaviour Recognition System for Human-Centric Software Engineering
Tool Demonstrations
DI SHAO School of Information Technology, Deakin University, Xiao Liu School of Information Technology, Deakin University, Ben Cheng School of Information Technology, Deakin University, Yi Wang School of Information Technology, Deakin University, Thuong Hoang School of Information Technology, Deakin University