| Comparing agent-oriented methodologies KH Dam, M Winikoff International Bi-Conference Workshop on Agent-Oriented Information Systems …, 2003 | 328 | 2003 |
| Deepjit: an end-to-end deep learning framework for just-in-time defect prediction T Hoang, HK Dam, Y Kamei, D Lo, N Ubayashi 2019 IEEE/ACM 16th International Conference on Mining Software Repositories …, 2019 | 242 | 2019 |
| 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 | 220 | 2018 |
| Explainable software analytics HK Dam, T Tran, A Ghose Proceedings of the 40th international conference on software engineering …, 2018 | 189 | 2018 |
| Automatic feature learning for predicting vulnerable software components HK Dam, T Tran, T Pham, SW Ng, J Grundy, A Ghose IEEE Transactions on Software Engineering 47 (1), 67-85, 2018 | 187 | 2018 |
| A deep tree-based model for software defect prediction HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim arXiv preprint arXiv:1802.00921, 2018 | 157 | 2018 |
| An empirical study of model-agnostic techniques for defect prediction models J Jiarpakdee, CK Tantithamthavorn, HK Dam, J Grundy IEEE Transactions on Software Engineering 48 (1), 166-185, 2020 | 147 | 2020 |
| A deep language model for software code HK Dam, T Tran, T Pham arXiv preprint arXiv:1608.02715, 2016 | 142 | 2016 |
| Automatic feature learning for vulnerability prediction HK Dam, T Tran, T Pham, SW Ng, J Grundy, A Ghose arXiv preprint arXiv:1708.02368, 2017 | 135 | 2017 |
| Simultaneous nitrification/denitrification and trace organic contaminant (TrOC) removal by an anoxic–aerobic membrane bioreactor (MBR) HV Phan, FI Hai, J Kang, HK Dam, R Zhang, WE Price, A Broeckmann, ... Bioresource technology 165, 96-104, 2014 | 120 | 2014 |
| Lessons learned from using a deep tree-based model for software defect prediction in practice HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim 2019 IEEE/ACM 16th International Conference on Mining Software Repositories …, 2019 | 112 | 2019 |
| Towards effective AI-powered agile project management HK Dam, T Tran, J Grundy, A Ghose, Y Kamei 2019 IEEE/ACM 41st international conference on software engineering: new …, 2019 | 98 | 2019 |
| Comparing particle swarms for tracking extrema in dynamic environments X Li, KH Dam The 2003 Congress on Evolutionary Computation, 2003. CEC'03. 3, 1772-1779, 2003 | 95 | 2003 |
| Strategic alignment of business processes ED Morrison, AK Ghose, HK Dam, KG Hinge, K Hoesch-Klohe Service-Oriented Computing-ICSOC 2011 Workshops: ICSOC 2011, International …, 2012 | 83 | 2012 |
| Predicting delivery capability in iterative software development M Choetkiertikul, HK Dam, T Tran, A Ghose, J Grundy IEEE Transactions on Software Engineering 44 (6), 551-573, 2017 | 64 | 2017 |
| Predicting delays in software projects using networked classification (t) M Choetkiertikul, HK Dam, T Tran, A Ghose 2015 30th IEEE/ACM International Conference on Automated Software …, 2015 | 60 | 2015 |
| Evaluating and comparing agent-oriented software engineering methodologies KH Dam School of Computer Science and Information Technology, RMIT University …, 2003 | 58* | 2003 |
| Predicting the delay of issues with due dates in software projects M Choetkiertikul, HK Dam, T Tran, A Ghose Empirical Software Engineering 22, 1223-1263, 2017 | 55 | 2017 |
| Towards a next-generation AOSE methodology HK Dam, M Winikoff Science of Computer Programming 78 (6), 684-694, 2013 | 52 | 2013 |
| Characterization and prediction of issue-related risks in software projects M Choetkiertikul, HK Dam, T Tran, A Ghose 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, 280-291, 2015 | 47 | 2015 |
Aditya GhoseProfessor of Computer Science, University of WollongongVerified email at uow.edu.au
