Deep Learning Models for Detecting Software Defects

Abstract

Software defect classification must be automated in order to ensure software dependability, as the use of automatic software-based applications grows. This chapter suggests an automatic software defect classification approach that is expert-based. The technique of grouping defects into predetermined categories is known as defect report categorization. Recently, numerous machine learning (ML) techniques have been introduced to classify defects into different categories. This study suggests a classification model that creates new word embeddings from defect reports using deep learning (DL) models, specifically the long short-term memory (LSTM) network, recurrent neural network (RNN), convolution neural network (CNN), and multilayer perceptron (MLP). The outcomes are compared to pre-trained word embedding using Google's word2vec in terms of recall, accuracy, and precision. The experimental results show that LSTM outperforms the other models used in the investigation. The maximum accuracy that LSTM can attain on the redmine dataset is 70%.