Code Snippets: Input sequences representing code snippets extracted from software projects, Q&A forums, and websites Expected Outputs: Corresponding error types or defect labels associated with each code snippet.

Utilizing Long Short-Term Memory (LSTM) architecture for sequence modeling. Embedding layers for token representation. Cross-entropy Loss for multi-class classification. Adam optimizer for efficient parameter updates.

1. Tokenization: Breaking down code snippets into individual tokens.
2. Label Encoding: Assigning numerical labels to error types for supervised learning.
3. Word Embedding and Padding: Mapping tokens to continuous vectors using embedding layers. Padding sequences to a fixed length for consistent input size.

Training Process: Iterative training with mini-batch processing. Hyperparameter tuning for optimal model performance.

Metrics: Assessing model accuracy, precision, recall, and confusion matrix. Flask Integration: Utilizing Flask for interactive visualizations. Enhancing interpretability through visual representations.

Proper tokenization and label encoding assume a meaningful representation of code semantics. Word embedding assumes capturing semantic relationships in the continuous vector space.

Explore additional architectures or ensemble methods for potential performance improvement. Continuously update and retrain the model with new data to adapt to evolving code patterns.

This approach combines LSTM-based neural networks with rigorous data preparation and evaluation processes to build a robust code defect prediction model. The emphasis is on leveraging deep learning techniques for effective defect identification in software development.

Code defects in more error-prone or risky areas based on code snippets.

AssertionError on line 1, AttributeError on line 1, AttributeError on line 3, AttributeError on line 4, FileNotFoundError on line 1, ImportError on line 1, IndentationError on line 2, IndexError on line 1, IndexError on line 2, KeyError on line 1, KeyError on line 2, ModuleNotFoundError on line 1, NameError on line 1, NameError on line 2, RecursionError on line 1, RecursionError on line 2, Success, SyntaxError on line 1, SyntaxError on line 2, TypeError on line 1, TypeError on line 2, TypeError on line 3, ValueError on line 1, ValueError on line 2, ZeroDivisionError on line 1, ZeroDivisionError on line 2, ZeroDivisionError on line 3,

The segment from the dataset