A hybrid deep learning approach with genetic and coral reefs metaheuristics for enhanced defect detection in software

Abstract

Early detection and correction of software defects is essential in the software development process. In the production stage, software with defects negatively impacts on operational costs and ultimately affects customer satisfaction. Although different approaches exist to predict software defects, two essential factors are timely and accurate detection. This paper presents a hybrid Deep Neural Network model for enhanced prediction of software bugs . Different Nature-Inspired Algorithms have been applied to improve the exploration of the hyperparameter solution space to optimize the Deep Neural Network architecture. Experimental investigations have been conducted using NASA dataset to predict software defects and evaluation measures like accuracy, computational time and F1 score have been used for performance comparison. The approach based on the combination of Genetic and Coral Reef metaheuristics outperformed all other models, achieving highest accuracy of around 96% and average F1-score of 0.92.

Keywords

• Software bug prediction
• Deep learning
• Neural networks
• Genetic algorithm
• Coral reefs optimization algorithm
• Nature inspired optimization techniques