Changeability prediction model for java class based on multiple layer perceptron neural network

2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2016-06-01 DOI:10.1109/ECTICON.2016.7561392

S. Rongviriyapanish, Thanapol Wisuttikul, Boonchai Charoendouysil, Pattarin Pitakket, Pattanan Anancharoenpakorn, Panita Meananeatra

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引用次数: 5

Abstract

A quality model for assessing the changeability level of java code is important for software development. It permits developer to know which classes to be improved for having a better software maintainability. Moreover, a good quality model must be created based on a set of well-selected attributes and metrics. Currently, no research work proposes a changeability assessment model that takes into consideration the metrics covering ten relevant object-oriented attributes. We propose a class changeability prediction model developed by using the multilayer perceptron (MLP) as a classifier method and a training data set of 137 java classes from jEdit open source project for training the model. Model accuracy attains 89.81% and the model can perfectly separate java classes with good changeability level from those with poor or fair changeability levels.

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基于多层感知器神经网络的java类可变性预测模型

用于评估java代码可变性级别的质量模型对于软件开发非常重要。它允许开发人员知道哪些类需要改进以获得更好的软件可维护性。此外，一个好的质量模型必须基于一组精心选择的属性和量度来创建。目前，还没有研究工作提出一个考虑涵盖十个相关面向对象属性的度量的可变性评估模型。采用多层感知器(multilayer perceptron, MLP)作为分类器方法，利用jEdit开源项目的137个java类的训练数据集对模型进行训练，建立了一个类可变性预测模型。模型的准确率达到89.81%，可以很好地将可变性好的java类与可变性差或一般的java类区分开来。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

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