• S Deepika
• Satish Dekka
• P Krishna Sai
• K Gowthama Laxmi
• S K Afreen Begam

Software systems have become larger and more complex than ever. Such characteristics make it be very challengeable to prevent software defects. Therefore, automatically predicting the number of defects in software modules is necessary and may help developers efficiently to allocate limited resources. Various approaches have been proposed to identify and fix such defects at minimal cost. However, the performances of these approaches require significant improvement. Therefore, we propose a novel approach that leverages deep learning techniques to predict the number of defects in software systems. First, we preprocess a publicly available dataset, including log transformation and data normalization. Second, we perform data modelling to prepare the data input for the deep learning model. Third, we pass the modelled data to a specially designed deep neural network-based model to predict the number of defects. We also evaluate the proposed approach on four well-known datasets. The evaluation results illustrate that the proposed approach is accurate and can improve upon the machine learning approach.

Machine Learning, Deep Learning, Fault, Minimal cost, Predictive modelling

"Software Fault Prediction Using Deep Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f462-f474, May-2024, Available :http://www.jetir.org/papers/JETIR2405554.pdf

"Software Fault Prediction Using Deep Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf462-f474, May-2024, Available at : http://www.jetir.org/papers/JETIR2405554.pdf