SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK - Eka Alifia Kusnanti; Lauretha Devi Fajar Vantie; Umi Laili Yuhana (Department of Informatics, Institut Teknologi Sepuluh Nopember, 2024)
Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).