Research Article ImpactofParameterTuningforOptimizingDeepNeuralNetwork Models for Predicting Software Faults Mansi Gupta , Kumar Rajnish , and Vandana Bhattacharjee Department of Computer Science and Engineering, BIT Mesra, Ranchi, India Correspondence should be addressed to Mansi Gupta; jv.mansi@gmail.com Received 13 October 2020; Revised 21 March 2021; Accepted 2 June 2021; Published 12 June 2021 Academic Editor: Jianping Gou Copyright©2021MansiGuptaetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Deepneuralnetworkmodelsbuiltbytheappropriatedesigndecisionsarecrucialtoobtainthedesiredclassifierperformance.is isespeciallydesiredwhenpredictingfaultpronenessofsoftwaremodules.Whencorrectlyidentified,thiscouldhelpinreducing thetestingcostbydirectingtheeffortsmoretowardsthemodulesidentifiedtobefaultprone.Tobeabletobuildanefficientdeep neuralnetworkmodel,itisimportantthattheparameterssuchasnumberofhiddenlayers,numberofnodesineachlayer,and trainingdetailssuchaslearningrateandregularizationmethodsbeinvestigatedindetail.eobjectiveofthispaperistoshowthe 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. 1.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 networkmodelsforsoftwarefaultpredictionbyfocusingon those aspects of training which impact the classifier per- formance the most. A comparison is made between the performances of deep neural network and other classifica- tion techniques such as na¨ıve Bayes, random forest, and decision tree. Software fault prediction is one of the major areas of investigation in the area of software quality [1]. Fault pre- diction being an intricate area of research, many software researchers and practitioners have experimented on nu- merous ways of predicting faults in software [2]. e ac- curatepredictionoffaultsincodeplaysaveryimportantrole as it can help in reducing test effort and costs and improve thequalityofsoftwaretoanextent.emaincauseoffailure of a software product is the defect in the code that occurs during the implementation of the software [3]. In an or- ganizationwherethebudgetislimited,thesoftwaremanager instead of going for complete software testing prefers for testing those modules that are fault prone using fault predictors. Software fault prediction methods initially used code metrics or simply software metrics and statistical approach for fault prediction. ereafter, the focus shifted to soft computing and machine learning (ML) techniques which tookoverallthepredictiontechniques[4].Insoftwarecode metrics-based methods, internal attributes of the software were measured for fault prediction. e commonly used software metrics’ suites were Quality Model For Object Oriented Design (QMOOD) metric suite [5], Chidamber and Kemerer (CK) metric suite [6], Metrics for Object Oriented Design (MOOD) metric suite [7], etc. From the perspective of machine learning, fault prediction comes under the classification task in which it discriminates faulty and nonfaulty modules [8]. Some representative ML methods are ensemble, support vector machine (SVM), naive Bayes, logistic regression, decision table, etc., and a review of such techniques applied to software fault pre- diction is given in [9]. In this work, a deep neural network model for software fault prediction is built and also several aspectsofthedeepneuralnetworkdesignareexplored.e role of number of layers, nodes in each layer, learning rate, Hindawi Scientific Programming Volume 2021, Article ID 6662932, 17 pages https://doi.org/10.1155/2021/6662932