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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.<\/jats:p>","DOI":"10.1155\/2021\/6662932","type":"journal-article","created":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T18:50:07Z","timestamp":1623523807000},"page":"1-17","source":"Crossref","is-referenced-by-count":18,"title":["Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3090-1216","authenticated-orcid":true,"given":"Mansi","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, BIT Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9263-3072","authenticated-orcid":true,"given":"Kumar","family":"Rajnish","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, BIT Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-2691","authenticated-orcid":true,"given":"Vandana","family":"Bhattacharjee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, BIT Mesra, Ranchi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"Dictionary learning based software defect prediction","author":"X.-Y. 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