Optimizing Defect Prediction in Python Programme: A Genetic Algorithm and Machine Learning Approach

Abstract

Software defect prediction is a vital aspect of software engineering, aiming to identify potential faults early in the development process to enhance quality and reduce maintenance costs [01]. Traditional defect prediction models often become outdated due to the dynamic nature of software development. This study introduces RK’s Enhanced Defect Prediction with Python Programs (EDPPP) model, which employs a hybrid approach combining neural networks with adaptive genetic algorithms to address the limitations of static models. The EDPPP model leverages the pattern recognition capabilities of neural networks and the optimization strengths of genetic algorithms, which evolve over generations to enhance feature selection and model parameters.
The adaptive genetic algorithm adjusts mutation rates based on the fitness of the population, ensuring continuous improvement and adaptability to changing data characteristics. By creating an initial population of binary feature vectors and iteratively refining them, the genetic algorithm fine-tunes the input features for the neural network, resulting in improved defect prediction accuracy. The model was evaluated using a real-world dataset, demonstrating its potential to significantly enhance software quality and reliability.
The promising results of the EDPPP model indicate its efficacy in providing a dynamic and adaptive solution for software defect prediction. This research highlights the importance of integrating advanced machine learning techniques to create robust and flexible prediction models, paving the way for future innovations in software engineering.