Hamoud Aljamaan

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 13

Top Topics

Learning Models

Automated Detection

Sentiment Analysis

Top Venues

Empir. Softw. Eng.

Autom. Softw. Eng.

Publications

Amal Alazba, Hamoud Aljamaan, Mohammad R. Alshayeb
CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection. Empir. Softw. Eng. 29 (3) (2024)
Amal Alazba, Hamoud Aljamaan, Mohammad R. Alshayeb
Automated detection of class diagram smells using self-supervised learning. Autom. Softw. Eng. 31 (1) (2024)
Rana Sandouka, Hamoud Aljamaan
Python code smells detection using conventional machine learning models. PeerJ Comput. Sci. 9 (2023)
Nouf Alturayeif, Hamoud Aljamaan, Jameleddine Hassine
An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learning. Autom. Softw. Eng. 30 (2) (2023)
Dorieh Alomari, Fatima Anis, Maryam Alabdullatif, Hamoud Aljamaan
A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models. EASE (2023)
Amal Alazba, Hamoud Aljamaan, Mohammad R. Alshayeb
Deep learning approaches for bad smell detection: a systematic literature review. Empir. Softw. Eng. 28 (3) (2023)
Norah Alsunaidi, Sara Aljbali, Yasmin Yasin, Hamoud Aljamaan
Arabic Cyberbullying Detection Using Machine Learning: State of the Art Survey. EASE (2023)
Kazi Ekramul Hoque, Hamoud Aljamaan
Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting. IEEE Access 9 (2021)
Hamoud Aljamaan
Voting Heterogeneous Ensemble for Code Smell Detection. ICMLA (2021)
Amal Alazba, Hamoud Aljamaan
Code smell detection using feature selection and stacking ensemble: An empirical investigation. Inf. Softw. Technol. 138 (2021)
Nouf Alturaief, Hamoud Aljamaan, Malak Baslyman
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation. ASE Workshops (2021)
Timothy C. Lethbridge, Andrew Forward, Omar Badreddin, Dusan Brestovansky, Miguel A. Garzón, Hamoud Aljamaan, Sultan Eid, Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Vahdat Abdelzad, Opeyemi Adesina, Aliaa Alghamdi, Abdulaziz Algablan, Amid Zakariapour
Umple: Model-driven development for open source and education. Sci. Comput. Program. 208 (2021)
Hamoud Aljamaan, Amal Alazba
Software defect prediction using tree-based ensembles. PROMISE (2020)
Hamoud Aljamaan, Timothy C. Lethbridge, Miguel A. Garzón
MOTL: a textual language for trace specification of state machines and associations. CASCON (2015)
Hamoud Aljamaan, Timothy Lethbridge, Miguel Garzón, Andrew Forward
UmpleRun: a Dynamic Analysis Tool for Textually Modeled State Machines using Umple. EXE@MoDELS (2015)
Miguel Garzón, Hamoud Aljamaan, Timothy C. Lethbridge
Umple: A framework for Model Driven Development of Object-Oriented Systems. SANER (2015)
Mahmoud O. Elish, Hamoud Aljamaan, Irfan Ahmad
Three empirical studies on predicting software maintainability using ensemble methods. Soft Comput. 19 (9) (2015)
Hamoud Aljamaan, Timothy C. Lethbridge, Omar Bahy Badreddin, Geoffrey Guest, Andrew Forward
Specifying Trace Directives for UML Attributes and State Machines. MODELSWARD (2014)
Miguel A. Garzón, Timothy C. Lethbridge, Hamoud Aljamaan, Omar Badreddin
Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach. CASCON (2014)
Vahdat Abdelzad, Hamoud Aljamaan, Opeyemi Adesina, Miguel Garzón, Timothy Lethbridge
A Model-Driven Solution for Financial Data Representation Expressed in FIXML. TTC@STAF (2014)
Omar Bahy Badreddin, Timothy C. Lethbridge, Andrew Forward, Maged Elaasar, Hamoud Aljamaan, Miguel Garzón
Enhanced Code Generation from UML Composite State Machines. MODELSWARD (2014)
Hamoud Aljamaan, Mahmoud O. Elish, Irfan Ahmad
An Ensemble of Computational Intelligence Models for Software Maintenance Effort Prediction. IWANN (1) (2013)
Hamoud Aljamaan, Timothy C. Lethbridge
Towards Tracing at the Model Level. WCRE (2012)
Ahmed A. Bahjat, Hamoud Aljamaan, Mohammad Alshayeb
SQL-Guard Design Pattern. SEDE (2009)
Hamoud Aljamaan, Mahmoud O. Elish
An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software. CIDM (2009)