Predicting Quality of Object-Oriented Systems through a Quality Model based on Design Metrics and Data Mining Techniques

被引:3
作者
Loh, Chuan Ho [1 ]
Lee, Sai Peck [1 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Software Engn, Kuala Lumpur 50603, Malaysia
来源
2009 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND ENGINEERING, PROCEEDINGS | 2009年
关键词
classification; clustering; data mining; object-orientation; design metrics; SOFTWARE;
D O I
10.1109/ICIME.2009.78
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most of the existing object-oriented design metrics and data mining techniques capture similar dimensions in the data sets, thus reflecting the fact that many of the metrics are based on similar hypotheses, properties, and principles. Accurate quality models can be built to predict the quality of object-oriented systems by using a subset of the existing object-oriented design metrics and data mining techniques. We propose a software quality model, namely QUAMO (QUAlity MOdel) which is based on divide-and-conquer strategy to measure the quality of object-oriented systems through a set of object-oriented design metrics and data mining techniques. The primary objective of the model is to make similar studies on software quality more comparable and repeatable. The proposed model is augmented from five quality models, namely McCall Model, Boehm Model, FURPS/FURPS+ (i.e. functionality, usability, reliability, performance, and supportability), ISO 9126, and Dromey Model. We empirically evaluated the proposed model on several versions of JUnit releases. We also used linear regression to formulate a prediction equation. The technique is useful to help us interpret the results and to facilitate comparisons of results from future similar studies.
引用
收藏
页码:239 / 243
页数:5
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