| Three empirical studies on predicting software maintainability using ensemble methods MO Elish, H Aljamaan, I Ahmad Soft Computing 19, 2511-2524, 2015 | 76 | 2015 |
| Software defect prediction using tree-based ensembles H Aljamaan, A Alazba Proceedings of the 16th ACM International Conference on Predictive Models …, 2020 | 75 | 2020 |
| Bad smell detection using machine learning techniques: a systematic literature review A Al-Shaaby, H Aljamaan, M Alshayeb Arabian Journal for Science and Engineering 45 (4), 2341-2369, 2020 | 70 | 2020 |
| An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software HI Aljamaan, MO Elish 2009 IEEE symposium on computational intelligence and data mining, 187-194, 2009 | 54 | 2009 |
| Impact of hyperparameter tuning on machine learning models in stock price forecasting KE Hoque, H Aljamaan IEEE Access 9, 163815-163830, 2021 | 53 | 2021 |
| Code smell detection using feature selection and stacking ensemble: An empirical investigation A Alazba, H Aljamaan Information and Software Technology, 106648, 2021 | 51 | 2021 |
| Umple: Model-driven development for open source and education TC Lethbridge, A Forward, O Badreddin, D Brestovansky, M Garzon, ... Science of Computer Programming 208, 102665, 2021 | 37 | 2021 |
| Umple: A framework for model driven development of object-oriented systems MA Garzón, H Aljamaan, TC Lethbridge 2015 ieee 22nd international conference on software analysis, evolution, and …, 2015 | 37 | 2015 |
| Enhanced Code Generation from UML Composite State Machines O Badreddin, TC Lethbridge, A Forward, M Elaasar, H Aljamaan, ... 2nd International Conference on Model-Driven Engineering and Software …, 2014 | 35 | 2014 |
| Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles A Alazba, H Aljamaan Applied Sciences 12 (9), 4577, 2022 | 23 | 2022 |
| An ensemble of computational intelligence models for software maintenance effort prediction H Aljamaan, MO Elish, I Ahmad Advances in Computational Intelligence: 12th International Work-Conference …, 2013 | 23 | 2013 |
| Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach. MA Garzón, TC Lethbridge, H Aljamaan, O Badreddin CASCON 14, 91-105, 2014 | 20 | 2014 |
| Specifying Trace Directives for UML Attributes and State Machines H Aljamaan, TC Lethbridge, O Badreddin, G Guest, A Forward 2nd International Conference on Model-Driven Engineering and Software …, 2014 | 19 | 2014 |
| Aware: Aspect-based sentiment analysis dataset of apps reviews for requirements elicitation N Alturaief, H Aljamaan, M Baslyman 2021 36th IEEE/ACM International Conference on Automated Software …, 2021 | 18 | 2021 |
| Deep learning approaches for bad smell detection: a systematic literature review A Alazba, H Aljamaan, M Alshayeb Empirical Software Engineering 28 (3), 77, 2023 | 15 | 2023 |
| Python code smells detection using conventional machine learning models R Sandouka, H Aljamaan PeerJ Computer Science 9, e1370, 2023 | 12 | 2023 |
| MOTL: a textual language for trace specification of state machines and associations H Aljamaan, TC Lethbridge, MA Garzón Proceedings of the 25th Annual International Conference on Computer Science …, 2015 | 12 | 2015 |
| Towards tracing at the model level H Aljamaan, TC Lethbridge 2012 19th Working Conference on Reverse Engineering, 495-498, 2012 | 11 | 2012 |
| Voting heterogeneous ensemble for code smell detection H Aljamaan 2021 20th IEEE international conference on machine learning and applications …, 2021 | 9 | 2021 |
| UmpleRun: a Dynamic Analysis Tool for Textually Modeled State Machines using Umple. H Aljamaan, T Lethbridge, M Garzón, A Forward EXE@ MoDELS, 16-20, 2015 | 8 | 2015 |
Timothy LethbridgeProfessor of Software Engineering and Computer Science, University of Ottawa, CanadaVerified email at uottawa.ca
