Dr Mahir Arzoky
Lecturer in Computer Science
Wilfrd Brown Building 218
- Email: email@example.com
- Tel: +44 (0)1895 265471
DR MAHIR ARZOKY is a Research Fellow in the College of Engineering, Design and Physical Sciences working on a research project titled Assessing The Quality Of Test Suites In Industrial Code (AQUATIC). Prior to this, he was a Research Associate in Machine Learning at the Cognitive Digital System Engineering Centre, Birmingham City University. Dr Arzoky obtained his PhD from the Department of Computer Science at Brunel University London in 2015. It was centred on applying Search Based Software Engineering and Intelligent Data Analysis techniques to a large real world software engineering dataset to model development trends and to predict changes. His research interest lies in the areas of Artificial Intelligence, Intelligent Data Analysis, Data Mining and Software Engineering, in specific Search Based Software Engineering.
Newest selected publications
Capiluppi, A., Ajienka, N., Ali, N., Arzoky, M., Counsell, S., Destefanis, G., (2020) 'Using the Lexicon from Source Code to Determine Application Domains'.International Conference on Evaluation and Assessment in Software Engineering. Trondheim, Norway. 17 - 17 April. ACM. pp. 110 - 119.Open Access Linket al.
Yousefi, L., Swift, S., Arzoky, M., Saachi, L., Chiovato, L. and Tucker, A. (2020) 'Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules'. Computational Intelligence. ISSN: 0824-7935 Open Access Link
Counsell, S., Swift, S., Arzoky, M. and Destefanis, G. (2020) 'Is complexity of re-test a reason why some refactorings are buggy? an empirical perspective'. Springer International Publishing. pp. 83 - 90. ISSN: 1865-0929
Amer Jid Almahri, F., Bell, D. and Arzoky, M. (2019) 'Personas Design For Conversational Systems In Education'. Informatics, 6 (4). pp. 46 - 46. ISSN: 2227-9709 Open Access Link
Shepperd, M., Guo, Y., Li, N., Arzoky, M., Capiluppi, A., Counsell, S., (2019) 'The Prevalence of Errors in Machine Learning Experiments'.20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). Springer International Publishing. pp. 102 - 109. ISSN: 0302-9743 Open Access Linket al.