Hamoud Aljamaan

Orcid: 0000-0002-2146-9348

Affiliations:
  • King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
  • University of Ottawa, Canada (PhD)


According to our database1, Hamoud Aljamaan authored at least 24 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Automated detection of class diagram smells using self-supervised learning.
Autom. Softw. Eng., June, 2024

2023
An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learning.
Autom. Softw. Eng., November, 2023

Deep learning approaches for bad smell detection: a systematic literature review.
Empir. Softw. Eng., June, 2023

Python code smells detection using conventional machine learning models.
PeerJ Comput. Sci., 2023

Arabic Cyberbullying Detection Using Machine Learning: State of the Art Survey.
Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 2023

A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models.
Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 2023

2021
Umple: Model-driven development for open source and education.
Sci. Comput. Program., 2021

Code smell detection using feature selection and stacking ensemble: An empirical investigation.
Inf. Softw. Technol., 2021

Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting.
IEEE Access, 2021

AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation.
Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, 2021

Voting Heterogeneous Ensemble for Code Smell Detection.
Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020
Software defect prediction using tree-based ensembles.
Proceedings of the PROMISE '20: 16th International Conference on Predictive Models and Data Analytics in Software Engineering, 2020

2015
Three empirical studies on predicting software maintainability using ensemble methods.
Soft Comput., 2015

Umple: A framework for Model Driven Development of Object-Oriented Systems.
Proceedings of the 22nd IEEE International Conference on Software Analysis, 2015

UmpleRun: a Dynamic Analysis Tool for Textually Modeled State Machines using Umple.
Proceedings of the 1st International Workshop on Executable Modeling co-located with ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS 2015), 2015

MOTL: a textual language for trace specification of state machines and associations.
Proceedings of 25th Annual International Conference on Computer Science and Software Engineering, 2015

2014
A Model-Driven Solution for Financial Data Representation Expressed in FIXML.
Proceedings of the 7th Transformation Tool Contest part of the Software Technologies: Applications and Foundations (STAF 2014) federation of conferences, 2014

Enhanced Code Generation from UML Composite State Machines.
Proceedings of the MODELSWARD 2014 - Proceedings of the 2nd International Conference on Model-Driven Engineering and Software Development, Lisbon, Portugal, 7, 2014

Specifying Trace Directives for UML Attributes and State Machines.
Proceedings of the MODELSWARD 2014 - Proceedings of the 2nd International Conference on Model-Driven Engineering and Software Development, Lisbon, Portugal, 7, 2014

Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach.
Proceedings of 24th Annual International Conference on Computer Science and Software Engineering, 2014

2013
An Ensemble of Computational Intelligence Models for Software Maintenance Effort Prediction.
Proceedings of the Advances in Computational Intelligence, 2013

2012
Towards Tracing at the Model Level.
Proceedings of the 19th Working Conference on Reverse Engineering, 2012

2009
SQL-Guard Design Pattern.
Proceedings of the 18th International Conference on Software Engineering and Data Engineering (SEDE-2009), 2009

An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software.
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2009


  Loading...