Datasets | Hamoud Aljamaan

DIVE: A Multi-Label Smart Contract Vulnerability Dataset

Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh

A multi-label dataset for smart contract vulnerability detection, containing 22,330 real-world Solidity contracts deployed between 2016 and 2024, annotated with eight co-occurring vulnerability types.

Paper Data

BibTeX

@article{alsunaidi2026dive,
  title     = {{DIVE}: A multi-label smart contract vulnerability dataset},
  author    = {Alsunaidi, Shikah J and Aljamaan, Hamoud and Hammoudeh, Mohammad},
  journal   = {Scientific Data},
  volume    = {13},
  year      = {2026},
  doi       = {10.1038/s41597-026-07025-5},
  publisher = {Nature Publishing Group}
}

SmellyCode++: Multi-Label Dataset for Code Smell Detection

Nawaf Alomari, Amal Alazba, Hamoud Aljamaan, Mohammad Alshayeb

An extended multi-label dataset for detecting code smells in Java and Python projects, containing 107,554 samples covering four smell types: God Class, Data Class, Feature Envy, and Long Method.

Paper Data

BibTeX

@article{alomari2025smellycode,
  title     = {{SmellyCode++}: Multi-label dataset for code smell detection},
  author    = {Alomari, Nawaf and Alazba, Amal and Aljamaan, Hamoud and Alshayeb, Mohammad},
  journal   = {Scientific Data},
  volume    = {12},
  number    = {1},
  year      = {2025},
  doi       = {10.1038/s41597-025-05465-z},
  publisher = {Nature Publishing Group}
}

Python Code Smells Dataset

Rana Sandouka, Hamoud Aljamaan

A dataset of Python source files annotated with code smell labels, used to benchmark conventional machine learning models for smell detection in Python projects.

Paper Data

BibTeX

@article{sandouka2023python,
  title     = {Python code smells detection using conventional machine learning models},
  author    = {Sandouka, Rana and Aljamaan, Hamoud},
  journal   = {PeerJ Computer Science},
  volume    = {9},
  pages     = {e1370},
  year      = {2023},
  doi       = {10.7717/peerj-cs.1370},
  publisher = {PeerJ}
}

AWARE: Aspect-Based Sentiment Analysis Dataset of App Reviews

Nouf Alturayeif, Hamoud Aljamaan, Malak Baslyman

A dataset of 11,323 annotated mobile app store reviews labeled with aspect terms, categories, and sentiment polarity, designed to support requirements elicitation from user feedback.

Paper Data

BibTeX

@inproceedings{alturayeif2021aware,
  title        = {{AWARE}: Aspect-based sentiment analysis dataset of apps reviews
                  for requirements elicitation},
  author       = {Alturayeif, Nouf and Aljamaan, Hamoud and Baslyman, Malak},
  booktitle    = {2021 36th IEEE/ACM International Conference on Automated
                  Software Engineering (ASE)},
  year         = {2021},
  doi          = {10.1109/ASE51524.2021.9679823},
  organization = {IEEE}
}