Comparison of back propagation training algorithms for software defect prediction

Arora, Ishani; Saha, Anju

doi:10.1109/ic3i.2016.7917934

Cited by 15 publications

(14 citation statements)

References 22 publications

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“…BRM approach works similar to the Levenberg-Marquardt optimization, in a sense that it minimises the squared errors and the weights and finds the optimal combination so that the network performs well. Bayesian based NN training is more robust than the standard back propagation nets [52]. When the parameters (weights and biases) increase, the network loses its ability to generalize.…”

Section: Bayesian Regularization Backpropagation Algorithmmentioning

confidence: 99%

The design of an indirect method for the human presence monitoring in the intelligent building

Vaňuš

Machac

Martínek

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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This article describes the design and verification of the indirect method of predicting the course of CO 2 concentration (ppm) from the measured temperature variables T indoor (°C) and the relative humidity rH indoor (%) and the temperature T outdoor (°C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to

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Section: Bayesian Regularization Backpropagation Algorithmmentioning

confidence: 99%

The design of an indirect method for the human presence monitoring in the intelligent building

Vaňuš

Machac

Martínek

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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“…Hence, the results proved that Bayesian Regularization gave better results as compared with the other two approaches. is finding is similar to [28,50] but different from [33]. GSD faces heterogeneous work environments that results in multiple challenges which have been highlighted in this research study.…”

Section: Discussionmentioning

confidence: 54%

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

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The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

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“…BR is also known as one of the widely used learning techniques in BP processes. The optimization process in a BR learning technique is similar to the learning technique of the LM method as it tunes the weights, minimizes errors, and finally extracts the best combination so that the ANN can perform better [37,38]. The SCG technique uses a mechanism called step-size scaling that decreases the time used inline searching in each learning iteration [39].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Empowered Software Defect Prediction System

Daoud¹,

Aftab²,

Ahmad³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of the extensively used machine-learning techniques for predicting defect-prone software modules. In this paper, a cloud-based framework for real-time softwaredefect prediction is presented. In the proposed framework, empirical analysis is performed to compare the performance of four training algorithms of the backpropagation technique on software-defect prediction: Bayesian regularization (BR), Scaled Conjugate Gradient, Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton, and Levenberg-Marquardt algorithms. The proposed framework also includes a fuzzy layer to identify the best training function based on performance. Publicly available cleaned versions of NASA datasets are used in this study. Various measures are used for performance evaluation including specificity, precision, recall, F-measure, an area under the receiver operating characteristic curve, accuracy, R 2 , and mean-square error. Two graphical user interface tools are developed in MatLab software to implement the proposed framework. The first tool is developed for comparing training functions as well as for extracting the results; the second tool is developed for the selection of the best training function using fuzzy logic.

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Comparison of back propagation training algorithms for software defect prediction

Cited by 15 publications

References 22 publications

The design of an indirect method for the human presence monitoring in the intelligent building

The design of an indirect method for the human presence monitoring in the intelligent building

Risk Prediction by Using Artificial Neural Network in Global Software Development

Machine Learning Empowered Software Defect Prediction System

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