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Personal profile

Personal Statement

My research is primarily focused on the area of software engineering, and in particular the development and evaluation of techniques to support the construction and evolution of more reliable and robust software systems. A common theme in much of this work is the application of machine learning to software engineering problems; for example, to automatically generate program test data, predict software project costs, perform intrusion detection, identify the root location of faults within systems, and automatically detect software system failures. The latter of these in particular makes extensive use of a range of both semi-supervised and unsupervised (clustering) machine learning algorithms to detect anomalous entries in large very high-dimensional and complex data sets. More recently I have also been turning my attention to the converse problem of testing AI systems.

My expertise and interests in machine learning extend outside the software engineering domain and I have employed clustering and classification algorithms in a variety of other contexts such as the automatic identification of potential road accident blackspots from crowdsourced smartphone sensor data, and the detection of objects within images.

I also have extensive experience of using machine learning in a variety of industrial projects such as forecasting customer buyer behaviour, predicting building energy performance, and modelling interventions to combat sedentary behaviour.

Teaching Interests

Over my career I have taught a lare range of classes, from 1st year undergraduate to postgraduate, mainly on areas related to programming, software engineering, software design, data analytics and machine learning.

My main current teaching responsibilities are:

I am also responsible for overseeing the MEng final year group project:

Expertise & Capabilities

  • Software Engineering (particularly design, testing and debugging)
  • Data Analytics
  • Machine Learning
  • Conducting empirical studies of software engineering techniques and processes
  • Search-based software engineering
  • Software analytics (static analysis, dynamic analysis and repository mining)

Research Interests

  • Software engineering
  • Machine Learning

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Projects

Research Output

  • Application of ensemble techniques in predicting object-oriented software maintainability

    Alsolai, H. & Roper, M., 15 Apr 2019, Proceedings of EASE 2019 - Evaluation and Assessment in Software Engineering. New York, p. 370-373 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution book

    Open Access
    File
  • 9 Downloads (Pure)

    Thesis

    Automated software development and model generation by means of syntactic and semantic analysis

    Author: Meiklejohn, M., 2 Apr 2015

    Supervisor: Roper, R. (Supervisor) & Wood, M. (Supervisor)

    Student thesis: Doctoral Thesis

    Activities

    • 2 Journal or guest editorship
    • 1 Organiser of major conference
    • 1 Organiser of special symposia

    ESEM 2011: 5th International Symposium on Empirical Software Engineering and Measurement

    Marc Roper (Member of programme committee)
    2011 → …

    Activity: Participating in or organising an event typesOrganiser of major conference

    Testing: Academic and Industrial Conference - Practice And Research Techniques (TAICPART)

    Marc Roper (Chair)
    29 Aug 200831 Aug 2008

    Activity: Participating in or organising an event typesOrganiser of special symposia