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A parallel framework for software defect detection and metric selection on cloud computing

journal contribution
posted on 2017-09-01, 00:00 authored by M M Ali, Shamsul HudaShamsul Huda, Jemal AbawajyJemal Abawajy, S Alyahya, H Al-Dossari, John YearwoodJohn Yearwood
With the continued growth of Internet of Things (IoT) and its convergence with the cloud, numerous interoperable software are being developed for cloud. Therefore, there is a growing demand to maintain a better quality of software in the cloud for improved service. This is more crucial as the cloud environment is growing fast towards a hybrid model; a combination of public and private cloud model. Considering the high volume of the available software as a service (SaaS) in the cloud, identification of non-standard software and measuring their quality in the SaaS is an urgent issue. Manual testing and determination of the quality of the software is very expensive and impossible to accomplish it to some extent. An automated software defect detection model that is capable to measure the relative quality of software and identify their faulty components can significantly reduce both the software development effort and can improve the cloud service. In this paper, we propose a software defect detection model that can be used to identify faulty components in big software metric data. The novelty of our proposed approach is that it can identify significant metrics using a combination of different filters and wrapper techniques. One of the important contributions of the proposed approach is that we designed and evaluated a parallel framework of a hybrid software defect predictor in order to deal with big software metric data in a computationally efficient way for cloud environment. Two different hybrids have been developed using Fisher and Maximum Relevance (MR) filters with a Artificial Neural Network (ANN) based wrapper in the parallel framework. The evaluations are performed with real defect-prone software datasets for all parallel versions. Experimental results show that the proposed parallel hybrid framework achieves a significant computational speedup on a computer cluster with a higher defect prediction accuracy and smaller number of software metrics compared to the independent filter or wrapper approaches.

History

Journal

Cluster computing

Volume

20

Issue

3

Pagination

2267 - 2281

Publisher

Springer

Location

New York, N.Y.

ISSN

1386-7857

eISSN

1573-7543

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, Springer