Human Aspects of Software Engineering Lab
The Human Aspects of Software Engineering Lab at the University of Auckland carries out research projects focused on improving software practices and processes. Our goal is to make it easier (and more fun!) for software teams to create user-friendly products. For example, we study ways to improve coordination on software teams, investigate novel techniques to help software developers understand what users want from the software, and study software developer work patterns. We employ a variety of both qualitative and quantitative research methods including developing new methods and tools, data mining, repository analysis, social network analysis, interviews, surveys, and lab experiments.
We are also interested in leveraging deep learning algorithms to build innovative software systems aiming to enable the disabled. Among these interests are enabling computers to understand individuals suffering from speech impairments to act as a communication medium on their behalf, improving and expediating the identification of autistic individuals, and other related assistive technologies that fall in similar scope.
Dr Kelly Blincoe has been awarded a Royal Society Te Apārangi Rutherford Discovery Fellowship for her research programme titled 'Towards more inclusive software engineering practices and tools to retain women in software engineering'. These prestigious grants support...
HASEL members, Haris Mumtaz and Kelly Blincoe, in collaboration with Paramvir Singh from the School of Computer Science (University of Auckland), recently studied the relationship between social and technical (design) aspects of software development. When technical...
Online feedback left by software users can help software teams improve their products. Feedback useful for product improvements exists online in app store reviews, tweets, and many other sources. Understanding the topics mentioned in this feedback is vitally important to maintaining a responsive, attractive software product so development teams know where to focus their product improvements. One way in which topics can automatically be detected is by performing text clustering, a common natural language processing technique that can group similar feedback together.
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