Browsing by Author "Messaoudi Chouaib"

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    Improving Code Quality based on AI
    (2024) Messaoudi Chouaib
    This thesis addresses the development of a LSTM model for correcting erroneous Python code snippets. The methodology comprises several key steps. First, a carefully curated dataset containing 450 incorrect Python codes with corresponding corrections was constructed, covering a diverse range of code patterns and functionalities. The dataset, structured as a CSV file, encapsulates various aspects of Python programming, including basic function definitions, mathematical calculations, and data processing tasks such as string manipulation (Dataset Construction). Next, the dataset underwent meticulous data processing, involving tokenization, padding, and splitting into training and testing sets to prepare it for model training (Data Processing). The model architecture, following an encoder-decoder framework with Long Short-Term Memory (LSTM) layers and a Dense layer for output, was designed to facilitate effective sequence generation (Model Architecture). Subsequently, the model underwent training using an iterative optimization algorithm such as stochastic gradient descent (SGD), with parameters adjusted based on the gradients of the loss function computed using backpropagation (Training). Evaluation of the trained model's performance on a separate test dataset revealed robust performance metrics, including high accuracy scores and low test loss values, indicating the model's effectiveness in distinguishing between correct and incorrect code snippets (Evaluation). The results underscore the potential of the developed classification and correction models to revolutionize various aspects of software development, including code validation processes and educational platforms, with implications for automated code analysis and correction in real-world applications (Conclusion).