I am a PhD candidate in SEGAL at the University of Victoria. I hold a Bachelor of Computer Science (Business option) from the University of Waterloo, Ontario and a Master of Mathematics, also from the University of Waterloo, under the supervision of Dr. Daniel Berry. I spent 12+ years working in the software engineering industry in various countries prior to joining SEGAL in August, 2018.
My research is focused on solving practical industry specific software engineering problems, with an emphasis on requirements engineering. I have also performed research using sentiment analysis tools and machine learning to help predict customer support ticket escalations. Currently, I am researching the continuous software development methodology, in particular, we are researching the scalability of continuous software development with respect to on-boarding, requirements management, development, testing, deployment, and customer feedback. Our goal is to produce a set of best practices and metrics for the development and use of continuous software development tools in a practical setting.
My other interests include theoretical and practical software engineering principles, including optimizations, code efficiency, algorithms, data structures, and simply writing cool code. As such, I also focus my efforts on helping train and educate the computer scientists of tomorrow to succeed in the software engineering realm in the real-world.
Colin Werner, Ze Shi Li, and Daniela Damian, Can A Machine Learn Through Customer Sentiment? A Cost-Aware Approach To Predict Support Ticket Escalations, in IEEE Software, vol. 36, no. 5, pp. 38-45, Sept.-Oct. 2019. DOI.
Colin Werner, Gabriel Tapuc, Lloyd Montgomery, Diksha Sharma, Sanja Dodos, and Daniela E. Damian. How angry are your customers? Sentiment analysis of support tickets that escalate. 2018 1st International Workshop on Affective Computing for Requirements Engineering (AffectRE), Banff, AB, 2018, pp. 1-8. DOI. (preprint)
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