David Bowes
David Bowes
Senior Lecturer, School of Computing & Communications, Lancaster University
Email verificata su lancaster.ac.uk
TitoloCitata daAnno
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
7742011
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
2172014
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
1412011
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
902014
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
International Conference on Engineering Applications of Neural Networks, 223-234, 2009
572009
Reflections on the NASA MDP data sets
D Gray, D Bowes, N Davey, Y Sun, B Christianson
IET software 6 (6), 549-558, 2012
532012
Software defect prediction: do different classifiers find the same defects?
D Bowes, T Hall, J Petrić
Software Quality Journal 26 (2), 525-552, 2018
402018
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
382012
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
342016
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
242016
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
242016
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
D Bowes, T Hall, D Gray
Automated Software Engineering 21 (2), 287-313, 2014
232014
Software defect prediction using static code metrics underestimates defect-proneness
D Gray, D Bowes, N Davey, Y Sun, B Christianson
The 2010 International Joint Conference on Neural Networks (IJCNN), 1-7, 2010
222010
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
212015
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
202017
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
202012
Further thoughts on precision
D Gray, D Bowes, N Davey, Y Sun, B Christianson
15th Annual Conference on Evaluation & Assessment in Software Engineering …, 2011
192011
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
172012
Developing fault-prediction models: What the research can show industry
T Hall, S Beecham, D Bowes, D Gray, S Counsell
IEEE software 28 (6), 96-99, 2011
172011
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
132017
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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