David Bowes

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Tracy HallProfessor of Software Engineering, Lancaster UniversityVerified email at lancaster.ac.uk
Steve CounsellProfessor, Brunel UniversityVerified email at brunel.ac.uk
Jean PetrićSenior Software Engineer at Jaguar Land RoverVerified email at jaguarlandrover.com
Sarah BeechamLero - The Irish Software Research CentreVerified email at lero.ie
Neil DaveyUniversity of HertfordshireVerified email at herts.ac.uk
Thomas ShippeyTeam Lead, Machine Learning at LogicMonitorVerified email at logicmonitor.com
Yi SunUniversity of HertfordshireVerified email at herts.ac.uk
Giuseppe DestefanisAssociate Professor, Brunel University, LondonVerified email at brunel.ac.uk
Emily Rowan WinterLancaster UniversityVerified email at lancaster.ac.uk
John.R.Woodward@gmail.com Head...Loughborough Uni. previously Head of OR LAB@QMUL, Stirling, Nottingham & BirminghamVerified email at lboro.ac.uk
Saemundur HaraldssonLecturer, University of StirlingVerified email at cs.stir.ac.uk
Marco OrtuAssistant Professor (RTT), Department of Business and Economics Sciences, University of CagliariVerified email at unica.it
Martin ShepperdProfessor of Software Technology & Modelling, Brunel University LondonVerified email at brunel.ac.uk
Serkan KirbasBloomberg LPVerified email at bloomberg.net
Michele MarchesiProfessor of Software Engineering (retired), DMI, Cagliari UniversityVerified email at unica.it
Burak TurhanProfessor, University of Oulu, FinlandVerified email at oulu.fi
Zaheed MahmoodUniversity of HertfordshireVerified email at herts.ac.uk
Alper SenFull Professor of Computer Engineering, Bogazici University, Istanbul, TurkeyVerified email at boun.edu.tr
Ayşe (Bener) BaşarToronto Metropolitan University (formerly Ryerson University)Verified email at torontomu.ca
Bora CaglayanHuawei European Research InstituteVerified email at huawei.com

David Bowes

Senior Lecturer, School of Computing & Communications, Lancaster University

Verified email at lancaster.ac.uk

Defect prediction


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A systematic literature review on fault prediction performance in software engineering T Hall, S Beecham, D Bowes, D Gray, S Counsell IEEE Transactions on Software Engineering 38 (6), 1276-1304, 2011	1431	2011
Researcher bias: The use of machine learning in software defect prediction M Shepperd, D Bowes, T Hall IEEE Transactions on Software Engineering 40 (6), 603-616, 2014	451	2014
The misuse of the NASA metrics data program data sets for automated software defect prediction D Gray, D Bowes, N Davey, Y Sun, B Christianson 15th annual conference on evaluation & assessment in software engineering …, 2011	237	2011
Some code smells have a significant but small effect on faults T Hall, M Zhang, D Bowes, Y Sun ACM Transactions on Software Engineering and Methodology (TOSEM) 23 (4), 1-39, 2014	226	2014
Software defect prediction: do different classifiers find the same defects? D Bowes, T Hall, J Petrić Software Quality Journal 26, 525-552, 2018	221	2018
Using the support vector machine as a classification method for software defect prediction with static code metrics D Gray, D Bowes, N Davey, Y Sun, B Christianson Engineering Applications of Neural Networks: 11th International Conference …, 2009	129	2009
Automatically identifying code features for software defect prediction: Using AST N-grams T Shippey, D Bowes, T Hall Information and Software Technology 106, 142-160, 2019	99	2019
Reflections on the NASA MDP data sets D Gray, D Bowes, N Davey, Y Sun, B Christianson IET software 6 (6), 549-558, 2012	87	2012
Comparing the performance of fault prediction models which report multiple performance measures: recomputing the confusion matrix D Bowes, T Hall, D Gray Proceedings of the 8th international conference on predictive models in …, 2012	74	2012
Mutation-aware fault prediction D Bowes, T Hall, M Harman, Y Jia, F Sarro, F Wu Proceedings of the 25th international symposium on software testing and …, 2016	72	2016
Building an ensemble for software defect prediction based on diversity selection J Petrić, D Bowes, T Hall, B Christianson, N Baddoo Proceedings of the 10th ACM/IEEE International symposium on empirical …, 2016	70	2016
SLuRp: a tool to help large complex systematic literature reviews deliver valid and rigorous results D Bowes, T Hall, S Beecham Proceedings of the 2nd international workshop on Evidential assessment of …, 2012	69	2012
How good are my tests? D Bowes, T Hall, J Petric, T Shippey, B Turhan 2017 IEEE/ACM 8th Workshop on Emerging Trends in Software Metrics (WETSoM), 9-14, 2017	67	2017
The jinx on the NASA software defect data sets J Petrić, D Bowes, T Hall, B Christianson, N Baddoo Proceedings of the 20th International Conference on Evaluation and …, 2016	65	2016
Mining communication patterns in software development: A github analysis M Ortu, T Hall, M Marchesi, R Tonelli, D Bowes, G Destefanis Proceedings of the 14th international conference on predictive models and …, 2018	54	2018
On the introduction of automatic program repair in Bloomberg S Kirbas, E Windels, O McBello, K Kells, M Pagano, R Szalanski, ... IEEE Software 38 (4), 43-51, 2021	50	2021
On measuring affects of github issues' commenters G Destefanis, M Ortu, D Bowes, M Marchesi, R Tonelli Proceedings of the 3rd International Workshop on Emotion Awareness in …, 2018	49	2018
What is the impact of imbalance on software defect prediction performance? Z Mahmood, D Bowes, PCR Lane, T Hall Proceedings of the 11th international conference on predictive models and …, 2015	46	2015
The state of machine learning methodology in software fault prediction T Hall, D Bowes 2012 11th international conference on machine learning and applications 2 …, 2012	43	2012
The relationship between evolutionary coupling and defects in large industrial software S Kirbas, B Caglayan, T Hall, S Counsell, D Bowes, A Sen, A Bener Journal of Software: Evolution and Process 29 (4), e1842, 2017	38	2017

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