Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Mansi Gupta,Vandana Bhattacharjee,Kumar Rajnish,Jianping Gou

doi:10.1155/2021/6662932

Mansi Gupta, Vandana Bhattacharjee + Show 2 more

Open Access

https://doi.org/10.1155/2021/6662932

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Journal: Scientific Programming	Publication Date: Jun 11, 2021
Citations: 7	License type: CC BY 4.0

Affiliation: Birla Institute of Technology, Mesra

Abstract

Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially desired when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more towards the modules identified to be fault prone. To be able to build an efficient deep neural network model, it is important that the parameters such as number of hidden layers, number of nodes in each layer, and training details such as learning rate and regularization methods be investigated in detail. The objective of this paper is to show the importance of hyperparameter tuning in developing efficient deep neural network models for predicting fault proneness of software modules and to compare the results with other machine learning algorithms. It is shown that the proposed model outperforms the other algorithms in most cases.

Highlights

Deep neural network (DNN) models have gained a lot of attention due to their outstanding performance in many tasks. e main aim of this study is to build deep neural network models for software fault prediction by focusing on those aspects of training which impact the classifier performance the most
The deep neural network with dropout provides the best result for recall and accuracy i.e., 1 and 92, respectively, whereas the deep neural network without dropout provides the best result in terms of the Fmeasure value. us, DNN with dropout outperforms all other classifiers in terms of accuracy
Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. is is especially desired when predicting fault proneness of software modules

Summary

Introduction

Deep neural network (DNN) models have gained a lot of attention due to their outstanding performance in many tasks. e main aim of this study is to build deep neural network models for software fault prediction by focusing on those aspects of training which impact the classifier performance the most. E main aim of this study is to build deep neural network models for software fault prediction by focusing on those aspects of training which impact the classifier performance the most. Li et al [22] proposed a framework called Defect Prediction via Convolutional Neural Network (DP-CNN) that used deep learning in order to effectively generate features. Manjula [39] presented an approach for software fault prediction In this approach, the genetic algorithm optimization process for feature subspace reduction was linked with the deep belief network for pattern learning. A broad experimental study was carried out which showed that the proposed approach achieved higher accuracy when compared with other state-of-the-art software fault prediction techniques. Is section discusses about brief overview of a generalized software fault prediction process, deep neural networks, parameter tuning process, L2 regularization, and dropout regularization.

Result

Results and Analysis

Conclusion and Future Scope