Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System

Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System

Racharla Suresh Kumar

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Prof. Bachala Sathyanarayana

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1 Sri Krishnadevaraya University

GJCST Volume 15 Issue G2

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The objective of our study was to evaluate, in a population of Togolese People Living With HIV(PLWHIV), the agreement between three scores derived from the general population namely the Framingham score, the Systematic Coronary Risk Evaluation (SCORE), the evaluation of the cardiovascular risk (CVR) according to the World Health Organization.

The earlier defect prediction and fault removal can play a vital role in ensuring software reliability and quality of service. In this paper Hybrid Evolutionary computing based Neural Network (HENN) based software defect prediction model has been developed. For HENN an adaptive genetic algorithm (A-GA) has been developed that alleviates the key existing limitations like local minima and convergence. Furthermore, the implementation of A-GA enables adaptive crossover and mutation probability selection that strengthens computational efficiency of our proposed system. The proposed HENN algorithm has been used for adaptive weight estimation and learning optimization in ANN for defect prediction. In addition, a novel defect prediction and fault removal cost estimation model has been derived to evaluate the cost effectiveness of the proposed system. The simulation results obtained for PROMISE and NASA MDP datasets exhibit the proposed model outperforms Levenberg Marquardt based ANN system (LM-ANN) and other systems as well. And also cost analysis exhibits that the proposed HENN model is approximate 21.66% cost effective as compared to LM-ANN.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Racharla Suresh Kumar. 2016. \u201cEvolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 15 (GJCST Volume 15 Issue G2): .

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GJCST Volume 15 Issue G2
Pg. 25- 38

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-D Classification: D.4.8

Submission ReceivedApril 13, 2015
RevisedApril 13, 2015
Peer Review Double Blind
Handling Editor
Accepted May 3, 2015
Published May 15, 2015

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v1.2

Issue date

January 7, 2016

Language

English

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Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - G: Interdisciplinary

Authors: Racharla Suresh Kumar, Prof. Bachala Sathyanarayana (PhD/Dr. count: 0)

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