Datasets | Hamoud Aljamaan

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.

Smart Contracts Vulnerability Detection Solidity Security Multi-label

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}
}

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.

Code Smells Java Python Multi-label Software Quality

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}
}

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

Code Smells Python Machine Learning Software Quality

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}
}

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.

Sentiment Analysis NLP App Reviews Requirements Engineering

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}
}

DIVE

SmellyCode++

Python Code Smells

AWARE