Smart contract vulnerabilities and bugs have become a key concern for
software engineers, as they can lead to significant financial losses,
reputational damage, and legal issues. Therefore, prioritizing bug fixing for
smart contracts is critical to maintaining trust. Due to the lack of tracking
tools, prioritizing smart contract-reported bugs is done manually, which is a
tedious task, limits bug triaging, and needs specialized knowledge. Towards
this end, we propose PrAIoritize; an automated approach for predicting smart
contract bug priorities that assist software engineers in prioritizing highly
urgent bug reports. PrAIoritize consists of two main phases: 1) automatic
labeling, which involves the automatic construction of a smart contract keyword
lexicon and the automatic assignment of priority levels to unlabeled bug
reports; 2) model construction, which involves feature engineering and designs
layers of feed-forward neural networks (FFNNs) and bidirectional long
short-term memory (BiLSTM) with multi-class classification to better capture
the features of the textual descriptions of bugs and predict their priority
levels. The model then is trained using smart contract bug reports collected
from two data sources: open-source software (OSS) projects available on GitHub
and NVD vulnerability database. Our evaluation demonstrates significant
improvement over state-of-the-art baselines and commonly used pre-trained
models (e.g. BERT) for similar classification tasks, with 5.75%-35.29% increase
in F-measure, precision, and recall