Professor Martin Shepperd
Prof of Software Technologies & Modelling
Wilfred Brown Building 114
- Email: email@example.com
- Tel: +44 (0)1895 267188
Martin Shepperd received a PhD in computer science from the Open University in 1991 for his work in measurement theory, many sorted algebras and their application to empirical software engineering. He was seconded to the Parliamentary Office of Science & Technology. Presently he is Head of Department and holds the chair of Software Technology and Modelling at Brunel University London, UK. He has published more than 150 refereed papers and three books in the areas of software engineering and machine learning. He is a fellow of the British Computer Society.
Previously Martin has worked as a software developer for HSBC.
I am General Chair of the 18th International Conference on Evaluation and Assessment in Software Engineering (EASE 2014).
I was General Chair of the 18th International Conference on Evaluation and Assessment in Software Engineering (EASE 2014)
Newest selected publications
Shepperd, M., Ajienka, N. and Counsell, S. (2018) 'The role and value of replication in empirical software engineering results'. INFORMATION AND SOFTWARE TECHNOLOGY, 99. pp. 120 - 132. ISSN: 0950-5849 Open Access Link
Shepperd, M. (2018) 'Replication studies considered harmful'.ICSE- NIER’18: 40th International Conference on Software Engineering: New Ideas and Emerging Results Track. Gothenburg. 27 - 3 June ISSN: 0270-5257 Open Access Link
Song, Q., Guo, Y. and Shepperd, M. (2018) 'A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction'. IEEE Transactions on Software Engineering. pp. 1 - 1. ISSN: 0098-5589 Open Access Link
Shepperd, M., Mair, C. and Jørgensen, M. (2017) 'An Experimental Evaluation of a De-biasing Intervention for ProfessionalSoftware Developers'.33rd ACM Software Applications Conference, 2018. Pau, France.Open Access Link
Shepperd, M., Hall, T. and Bowes, D. (2017) 'Authors' Reply to “Comments on 'Researcher Bias: The Use of Machine Learning in Software Defect Prediction' ”'. IEEE Transactions on Software Engineering. pp. 1 - 1. ISSN: 0098-5589 Open Access Link