Machine Learning (ML) is the discipline that studies methods for automatically
inferring models from data. Machine learning has been successfully applied in
many areas of software engineering ranging from behaviour extraction, to
testing, to bug fixing. However, there is comparatively less research on applying
software engineering techniques to designing and implementing machine
learning applications.
Machine learning techniques disrupt the traditional models of software
development and call for quicker, if not immediate, response from requirements
engineering (RE). Indeed, the social underpinning and the increasing reliance on
software systems for every aspect of our life, call for better methods to
understand the impact and implications of software solutions on the wellbeing of
individuals and society as a whole. The intrinsic ability of RE to deal with
conflicts, negotiation, and its traditional focus on tackling those wicked problems
is highly beneficial.
The seminar will review and reflect on the synergies between machine learning
and software engineering. In this seminar, I will introduce the principles of
machine learning, give an overview of some key methods, and present examples
of interaction between software engineering and machine learning. I will also
discuss some open challenges on how machine learning can benefit from
software engineering methods in general and requirements engineering in
particular.