MLP based Reusability Assessment Automation Model for Java based Software Systems

Автор: Surbhi Maggo, Chetna Gupta

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 8 vol.6, 2014 года.

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Reuse refers to a common principle of using existing resources repeatedly, that is pervasively applicable everywhere. In software engineering reuse refers to the development of software systems using already available artifacts or assets partially or completely, with or without modifications. Software reuse not only promises significant improvements in productivity and quality but also provides for the development of more reliable, cost effective, dependable and less buggy (considering that prior use and testing have removed errors) software with reduced time and effort. In this paper we present an efficient and reliable automation model for reusability evaluation of procedure based object oriented software for predicting the reusability levels of the components as low, medium or high. The presented model follows a reusability metric framework that targets the requisite reusability attributes including maintainability (using the Maintainability Index) for functional analysis of the components. Further Multilayer perceptron (using back propagation) based neural network is applied for the establishment of significant relationships among these attributes for reusability prediction. The proposed approach provides support for reusability evaluation at functional level rather than at structural level. The automation support for this approach is provided in the form of a tool named JRA2M2 (Java based Reusability Assessment Automation Model using Multilayer Perceptron (MLP)), implemented in Java. The performance of JRA2M2 is recorded using parameters like accuracy, classification error, precision and recall. The results generated using JRA2M2 indicate that the proposed automation tool can be effectively used as a reliable and efficient solution for automated evaluation of reusability.

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Back propagation, Maintainability Index, Metrics, Multi Layer Perceptron, Neural Network, Procedure Oriented, Reusability

Короткий адрес: https://sciup.org/15014680

IDR: 15014680

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