Application of Weighted Combinations of Activation Functions to Defect Prediction in Software Development

Abstract

Software defect prediction (SDP) method aims to identify potential bugs in programs or defective modules in software projects. SDP method can greatly help developers allocate needed testing- and debugging-efforts. Presently, SDP is typically divided into two procedures: extracting features from source code and building a classification model using machine learning methods, such as support vector machine, decision tree, and neural networks, to build a classifier for defect prediction. However, there are still some limitations for SDP. For example, machine learning models require a fixed input size, but the size of each program is mostly inconsistent. Activation functions play an essential role in the training of artificial neural networks, but every kind of activation function has its own particular strengths and inherent constraints. The main purpose of this article is to propose a general framework of combining different activation functions with given weights to improve the effectiveness of SDP. We construct 41 kinds of defect prediction models by deep belief networks (DBNs) built with double-weighted or triple-weighted combination of six most commonly used activation functions to improve the predictions. In the experiment of this study, some real data from open-source projects are selected to evaluate the performance of our proposed weighted combination methods. It is found experimentally that the weighted combinations methods can enhance the accuracy of SDP. It is also noticed that our proposed weighted combination scheme is not restricted to the DBN or a particular kind of activation functions.