| Data mining static code attributes to learn defect predictors T Menzies, J Greenwald, A Frank IEEE transactions on software engineering 33 (1), 2-13, 2007 | 1991 | 2007 |
| The promise repository of empirical software engineering data T Menzies, B Caglayan, E Kocaguneli, J Krall, F Peters, B Turhan June, 2012 | 1458* | 2012 |
| On the relative value of cross-company and within-company data for defect prediction B Turhan, T Menzies, AB Bener, J Di Stefano Empirical Software Engineering 14 (5), 540-578, 2009 | 892 | 2009 |
| Heterogeneous Defect Prediction J Nam, W Fu, S Kim, T Menzies, L Tan IEEE Transactions on Software Engineering 44 (9), 874 - 896, 0 | 650* | |
| Defect prediction from static code features: current results, limitations, new approaches T Menzies, Z Milton, B Turhan, B Cukic, Y Jiang, A Bener Automated Software Engineering 17 (4), 375-407, 2010 | 623 | 2010 |
| Automated severity assessment of software defect reports T Menzies, A Marcus 2008 IEEE International Conference on Software Maintenance, 346-355, 2008 | 446 | 2008 |
| On the value of ensemble effort estimation E Kocaguneli, T Menzies, JW Keung IEEE Transactions on Software Engineering 38 (6), 1403-1416, 2011 | 346 | 2011 |
| Problems with Precision: A Response to" comments on'data mining static code attributes to learn defect predictors'" T Menzies, A Dekhtyar, J Distefano, J Greenwald IEEE Transactions on Software Engineering 33 (9), 637-640, 2007 | 346 | 2007 |
| Selecting best practices for effort estimation T Menzies, Z Chen, J Hihn, K Lum IEEE Transactions on Software Engineering 32 (11), 883-895, 2006 | 346 | 2006 |
| Bias in Machine Learning Software: Why? How? What to do? J Chakraborty, S Majumder, T Menzies ESEC/FSE 2021 29th ACM Joint European Software Engineering Conference and …, 2021 | 342 | 2021 |
| What is wrong with topic modeling?(and how to fix it using search-based se) A Agrawal, W Fu, T Menzies Information and Software Technology 98 (June), 74-88, 2018 | 340 | 2018 |
| Local versus global lessons for defect prediction and effort estimation T Menzies, A Butcher, D Cok, A Marcus, L Layman, F Shull, B Turhan, ... IEEE Transactions on software engineering 39 (6), 822-834, 2012 | 336 | 2012 |
| A deep learning model for estimating story points M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, T Menzies IEEE Transactions on Software Engineering 45 (7), 637-656, 2018 | 290 | 2018 |
| Automatic query reformulations for text retrieval in software engineering S Haiduc, G Bavota, A Marcus, R Oliveto, A De Lucia, T Menzies 2013 35th International Conference on Software Engineering (ICSE), 842-851, 2013 | 283 | 2013 |
| Better cross company defect prediction F Peters, T Menzies, A Marcus 2013 10th working conference on mining software repositories (MSR), 409-418, 2013 | 274 | 2013 |
| Tuning for software analytics: Is it really necessary? W Fu, T Menzies, X Shen Information and Software Technology 76, 135-146, 2016 | 273 | 2016 |
| On the value of user preferences in search-based software engineering: A case study in software product lines AS Sayyad, T Menzies, H Ammar 2013 35Th international conference on software engineering (ICSE), 492-501, 2013 | 268 | 2013 |
| Easy over Hard: A Case Study on Deep Learning W Fu, T Menzies ESEC/SIGSOFT FSE 2017, 49-60, 2017 | 264 | 2017 |
| Implications of ceiling effects in defect predictors T Menzies, B Turhan, A Bener, G Gay, B Cukic, Y Jiang Proceedings of the 4th international workshop on Predictor models in …, 2008 | 259 | 2008 |
| Exploiting the essential assumptions of analogy-based effort estimation E Kocaguneli, T Menzies, A Bener, JW Keung IEEE transactions on software engineering 38 (2), 425-438, 2011 | 255 | 2011 |
Bojan CukicProfessor and Dean, College of Computing and Informatics, University of North Carolina at CharlotteVerified email at charlotte.edu
