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
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
University, UK. She received her PhD degree in Computer Science from the
University of Paris VI in 2013. Her research interests include dynamic mediator
synthesis for interoperability and collaborative security.
She was part of the Connect and EternalS EU projects that explored synergies
between machine learning and software synthesis.
The results of her work have been published in leading conferences and journals
such as Middleware, ECSA, and IEEE TSE. Bennaceur has been a member of the
program committee of several software engineering conferences including
RE:Next 2016 and ESEC/FSE 2015-NIER. She has been the program co-chair for
ESEC/FSE 2017 Artifact track and SEAMS 2019 and is co-chairing the Poster
Track at RE 2020.