Software Quality Prediction Model with the Aid of Advanced Neural Network with HCS

Sheoran, Kavita; Tomar, Pradeep; Mishra, Rajesh

doi:10.1016/j.procs.2016.07.365

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…In the study [15], the authors used a hybrid method of quality prediction. Predicting is conducted with using the advanced neural network integrated with the Hybrid Cuckoo search (HCS) optimization algorithm for improving the accuracy of forecast.…”

Section: Overview Of Known Models Methods and Tools For Software Qual...mentioning

confidence: 99%

Neural-Network Model of Software Quality Prediction Based on Quality Attributes

Lebiga

Hovorushchenko

KAPUSTIAN

2022

CSIT

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The paper proposes a neural-network model of software quality prediction based on quality attributes. The proposedmodel differs from the known models, because it provides considering the importance of each quality attribute and their interactionwithin each software quality characteristic. The artificial neural network (ANN) outputs correspond to the values of software qualitycharacteristics (functional suitability, performance efficiency, usability, reliability, compatibility, security, maintainabi lity, portability).The artificial neural network (ANN) outputs make it possible assessing the total impact of quality attributes on software qualitycharacteristics

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Section: Overview Of Known Models Methods and Tools For Software Qual...mentioning

confidence: 99%

Neural-Network Model of Software Quality Prediction Based on Quality Attributes

Lebiga

Hovorushchenko

KAPUSTIAN

2022

CSIT

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“…An Advanced Neural Network utilizing a Hybrid Cuckoo Search (HCS) optimization algorithm was used by Sheoran et al [4] for predicting maintainability and reliability factors of software quality.…”

Section: Information Flow Complexitymentioning

confidence: 99%

“…Yadav and Yadav [7] proposed a membership function for software metrics. Sheoran et al [4] have applied the advanced neural network technique for predicting software quality. Rizvi et al [5] proposed a fuzzy logic framework for quantifying software reliability.…”

Section: Introductionmentioning

confidence: 99%

A Review Report on Software Quality Measurement and Estimation

Berlas¹

2023

Preprint

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Software quality definition encompasses six key factors. These are low levels of defects, high reliability, efficient structure that minimizes insertion of new defects during defect repairs, effective customer support, high user satisfaction, and a high defect repair rate, especially for high-severity defects. Several methods have been proposed for measuring software quality. These include fuzzy logic based frameworks, neuro fuzzy models, unsupervised learning methods, queuing models, and software quality metrics. This research paper reviews different frameworks, models, metrics, and evaluation techniques that have been published in various research papers for estimating and assessing software quality. Research publications related to software quality measurement, estimation, and assessment were collected for the literature review. These publications were accessed from online research databases that consist of open access (OA) journals.

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“…To build an ANN with a selforganizing architecture and suitable learning algorithm for nonlinear system modeling, Han et al [21] developed an automatic axon-NN, which can self-organize the architecture and weights, thus improving network performance. Sheoran et al [22] proposed a hybrid method for software quality prediction, which used an advanced NN that incorporated a hybrid cuckoo search optimization algorithm for better prediction accuracy. Russo et al [4] applied optimal ANNs to air quality forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Zhang

2019

Complexity

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In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.

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Software Quality Prediction Model with the Aid of Advanced Neural Network with HCS

Cited by 7 publications

References 7 publications

Neural-Network Model of Software Quality Prediction Based on Quality Attributes

Neural-Network Model of Software Quality Prediction Based on Quality Attributes

A Review Report on Software Quality Measurement and Estimation

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

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