Workshop

With the exponential growth of social media platforms like Twitter, a seemingly vast amount of data has become available for mining to draw conclusions about various topics, including awareness systems requirements. The exchange of health-related information on social media has been heralded as a new way to explore information-seeking behaviour during pandemics and design and develop awareness systems that address the public’s information need. Online datasets such as Twitter, Google Trends and Reddit have several advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost.

In this paper, to explore the pandemic awareness systems’ requirements, we utilize data from the large accessible database of tweets and Reddit’s posts to explore the contextual patterns and temporal trends in Canadians’ information-seeking behaviour during the COVID-19 pandemic. To validate our inferences and to understand how google searches regarding COVID-19 were distributed throughout the course of the pandemic in Canada, we complement our Twitter and Reddit data with that collected through Google Trends, which tracks the popularity of specific search terms on Google. Our results show that Social media content contains useful technical information and can be used as a source to explore the requirements of pandemic awareness systems.

Esmaeil Shakeri

University of Calgary

Behrouz Far

University of Calgary

Workshop

Many individuals find it difficult to meet their personal health and wellbeing goals. To address this, mobile health (mHealth) apps aimed at improving individuals’ diet, physical activity, sleep and mental health, are emerging at an increasing pace. These modern digital health interventions are a promising solution to promote behaviour change and help people maintain better health while controlling rising healthcare expenditures. However, the real-life effects of mHealth apps are often overshadowed by high dropout rates, with the loss of participants during the intervention seeming to be the rule rather than the exception. We designed the mHealth4U model as a sample-based study of user requirement and design preferences to enable more targeted health and wellbeing self-management. This model is aimed at understanding how life-changing digital health interventions can be designed and what software design components might increase consumers’ acceptance, adherence and continuous engagement. We put forward three hypotheses in terms of designing an mHealth app that is consumer-centred: consumers prefer (1) self-management mHealth apps that target multiple key health and wellbeing dimensions, (2) intelligent recommendations, and (3) behaviour change support delivered precisely where, when and how it is needed most. We design the mHealth4U model around the 3U cyber security design components (user, usage and usability) and validate the hypotheses through a randomised sampling test with 114 participants. The results of this research will inform the design of a next-generation of digital health interventions capable of supporting the end-users to achieve the healthy lifestyle they deserve.

Hamza Sellak

CSIRO's Data61

Australia

Marthie Grobler

CSIRO's Data61

Workshop

For software projects, significant delays can result in heavy penalty which may end up with project costs exceeding their budgets. As a consequence,employees,i.e.,software developers,are often requested to work overtime in order to reduce or even eliminate the delays. By doing so, overtime payment may often be introduced and excessive overtime payment can also easily swallow company profit which may even lead to serious overdraft. Hence software manager needs to decide who should work overtime and how much overtime they would take in order to control the cost. This means that it is important to investigate how to reduce or eliminate the overall penalties by taking multiple concurrent software projects into account. In practice, there is normally a number of available employees with same or similar skills and domain knowledge from other similar concurrent projects. In addition,they have different skill proficiency. So rescheduling those employees with appropriate overtime may be feasible to find a solution which can reduce or eliminate the penalties of delayed software projects. Since this kind of scheduling is a typical NP-hard problem, a novel generic strategy is proposed to help select appropriate employees and determine how much overtime to be assigned to the delayed activities. The new strategy combines the features of ant colony optimization algorithm and Tabu strategy and includes four rules to reduce the search space. A set of comprehensive generic experiments is carried out in order to evaluate the performance of the proposed strategy in a general manner. In addition,three real world software project instances are also utilized to evaluate our strategy. The results demonstrate that our strategy is effective which outperforms the other representative strategies which are applied successfully at software project scheduling.

wei zhang

East China University of Technology

Yun Yang

Swinburne University of Technology

Xiao Liu

School of Information Technology, Deakin University

Australia

Workshop

Agile software development welcomes changes throughout software development - but this implies that agile teams face several dilemmas. When to respond to a change; how to respond; how to manage the change. Our current understanding and support for agile teams during such change management is very limited.

Psychological behavioral change models can be used to better understand the behavior of agile teams. Combining our understanding of agile teams and practices with a review of behavior change models, we propose several avenues for studying behavior and behavioral changes in agile teams. Our proposed interdisciplinary approach provides a much needed avenue to acknowledge and address the psychological and behavioral aspects of the humans central to the software engineering process, ultimately assisting with their well-being and productivity.

Kashumi Madampe

Monash University, Australia

Australia

Rashina Hoda

Monash University

Australia

John Grundy

Monash University, Australia

Australia

Workshop

Developers construct bioinformatics software to automate crucial analysis and research related to biological science. However, challenges while developing bioinformatics software can prohibit advancement in biological science research. Through a human-centric systematic analysis, we can identify challenges related to bioinformatics software development and envision future research directions. From our qualitative analysis with 221 Stack Overflow questions, we identify six categories of challenges: file operations, searching genetic entities, defect resolution, configuration management, sequence alignment, and translation of genetic information. To mitigate the identified challenges we envision three research directions that require synergies between bioinformatics and automated software engineering: (i) automated configuration recommendation using optimization algorithms, (ii) automated and comprehensive defect categorization, and (iii) intelligent task assistance with active and reinforcement learning.

Akond Rahman

Tennessee Tech University

United States

Farzana Ahamed Bhuiyan

Tennessee Tech University