Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

Racharla Suresh Kumar; Prof. Bachala Sathyanarayana

Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

Racharla Suresh Kumar * §

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Prof. Bachala Sathyanarayana

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§ Sri Krishnadevaraya University

Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

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CST18G37

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Abstract

To meet the requirement of an efficient software defect prediction,in this paper an evolutionary computing based neural network learning scheme has been developed that alleviates the existing Artificial Neural Network (ANN) limitations such as local minima and convergence issues. To achieve optimal software defect prediction, in this paper, Adaptive-Genetic Algorithm (A-GA) based ANN learning and weightestimation scheme has been developed. Unlike conventional GA, in this paper we have used adaptive crossover and mutation probability parameter that alleviates the issue of disruption towards optimal solution. We have used object oriented software metrics, CK metrics for fault prediction and the proposed Evolutionary Computing Based Hybrid Neural Network (HENN)algorithm has been examined for performance in terms of accuracy, precision, recall, F-measure, completeness etc, where it has performed better as compared to major existing schemes. The proposed scheme exhibited 97.99% prediction accuracy while ensuring optimal precision, Fmeasure and recall.

References

31 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Racharla Suresh Kumar, Prof. Bachala Sathyanarayana. 2015. "Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 15 (GJCST Volume 15 Issue D1).

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GJCST Volume 15 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification C.1.3 F.1.1

Submission ReceivedDecember 16, 2014
Peer Review Double Blind
Handling Editor
Accepted January 1, 2015
Published January 15, 2015

Version of record

v1.2

Issue date

November 4, 2015

Language

English

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Article Matrices

Total Score: 142

Country: India

Subject: Global Journal of Computer Science and Technology

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

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