A software developer works on many tasks per day, frequently switching between these tasks back and forth. This constant churn of tasks makes it difficult for a developer to know the specifics of when they worked on what task, complicating task resumption, planning, retrospection, and reporting activities. We introduce a new approach to help identify the topic of work for a given time interval that is based on capturing the contents of the developer’s active window at regular intervals and creating a vector representation of key information the developer viewed. To evaluate our approach, we created a data set with multiple developers working on the same set of six information seeking tasks that we also make available for other researchers to investigate similar approaches. Our analysis shows that our approach enables: 1) segments of a developer’s work to be automatically associated with a task from a known set of tasks with average accuracy of 70.6%, and 2) a visual representation describing a segment of work that a developer can use to recognize a task with average accuracy of 67.9%.

Chris Satterfield

University of British Columbia

Thomas Fritz

University of Zurich

Switzerland

Gail Murphy

University of British Columbia

Canada

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.

Zhiyuan Wan

Zhejiang University

China

Gail Murphy

University of British Columbia

Canada

Xin Xia

Monash University

Australia

Recognition of human behaviours including body motions and facial expressions plays a significant role in human-centric software engineering. However, due to the data and computation intensive nature of human behaviour recognition through video analytics, expensive powerful machines are often required, which could hinder the research and application in human-centric software engineering. To address such an issue, this paper proposes a cost-effective human behaviour recognition system named Edge4Real which can be easily deployed in an edge computing environment with commodity machines. Compared with existing centralised solutions, Edge4Real has three major advantages including cost-effectiveness, easy-to-use, and real-time. Specifically, Edge4Real adopts a distributed architecture where components such as motion capturing, human behaviour recognition, data decoding and extraction, and the application of the recognition result, can be deployed on separated end devices and edge nodes in an edge computing environment. Using a virtual reality application which can capture a user’s motion and translate into the motion of a 3D avatar in real time, we successfully validate the effectiveness of the system and demonstrate its promising value to the research and application of human-centric software engineering. The demo video can be found at https://youtu.be/tnEshD8j-kA.

DI SHAO

School of Information Technology, Deakin University

Xiao Liu

School of Information Technology, Deakin University

Australia

Ben Cheng

School of Information Technology, Deakin University

Owen Wang

School of Information Technology, Deakin University

Thuong Hoang

School of Information Technology, Deakin University