Fundamentals and Methods of Machine and Deep Learning

9A Novel Convolutional Neural Network Model to Predict Software Defects

Kumar Rajnish*, Vandana Bhattacharjee and Mansi Gupta

Department of CSE, BIT Mesra, Ranchi, India

Abstract

Machine learning (ML) is becoming increasingly important as a research tool due to its various frameworks and learning approaches. With the ever-increasing scale of software, reliability has become a crucial issue and software defect prediction is utilized to assist developers in finding potential defect and allocating their testing efforts. Traditional methods of software defect prediction mainly focus on designing static code metrics which are fed into ML classifiers to predict defects in the code. Even with the same ML techniques, many researchers apply statistical approaches to classify software modules and decide whether each module is defect prone or not and, accordingly, train their model. Deep neural network (DNN) and convolutional neural network (CNN) models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially significant when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more toward the modules identified to be fault prone. This paper proposed a Novel CNN (NCNN) model to predict software defects. The framework used is Python Programming Language with Keras and TensorFlow. A comparative analysis with ML algorithms [such as Random ...