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David Bowes
David Bowes
Senior Lecturer, School of Computing & Communications, Lancaster University
Verified email at lancaster.ac.uk
Title
Cited by
Cited by
Year
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
14542011
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
4592014
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
2412011
Software defect prediction: do different classifiers find the same defects?
D Bowes, T Hall, J Petrić
Software Quality Journal 26, 525-552, 2018
2362018
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
2342014
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
1332009
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
1082019
Reflections on the NASA MDP data sets
D Gray, D Bowes, N Davey, Y Sun, B Christianson
IET software 6 (6), 549-558, 2012
932012
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
792012
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
722017
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
712016
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
712016
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
712012
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
692016
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
572018
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
532021
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
522018
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
512015
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
472012
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
D Bowes, T Hall, D Gray
Automated Software Engineering 21, 287-313, 2014
402014
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