Paper:
Language: English:

MASP – An Enhanced Model of Fault Type Identification in Object-Oriented Software Engineering

Atchara Mahaweerawat^, Peraphon Sophatsathit^, Chidchanok Lursinsap^*, and Petr Musilek^**

^*Advanced Virtual and Intelligent Computing Center (AVIC), Department of Mathematics, Faculty of Science, Chulalongkorn University, Phyathai Road, Patumwan, Bangkok 10330, Thailand
^**Facility for Advanced Computational Intelligence and Applications (FACIA), Department of Electrical and Computer Engineering, Faculty of Engineering, University of Alberta, W2-030 ECERF, Edmonton, Alberta T6G 2V4, Canada

Received: February 22, 2005

Accepted: December 21, 2005

Keywords: software fault, predictive model, neural networks, fault metrics, fault prediction and identification

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.3 pp. 312-322, 2006

Abstract

To remain competitive in the dynamic world of software development, organizations must optimize the use of their limited resources to deliver quality products on time and within budget. This requires prevention of fault introduction and quick discovery and repair of residual faults.
In this paper, a new model for predicting and identifying of faults in object-oriented software systems is introduced. In particular, faults due to the use of inheritance and polymorphism are considered as they account for significant portion of faults in object-oriented systems.
The proposed MASP model acts as a fault metric selector that gathers relevant filtering metrics suitable for specific fault types employing coarse-grained and fine-grained metric selection algorithms. A fault predictor is subsequently established to identify the fault type of individual fault classification.
It is concluded that the proposed model yields high discrimination accuracy between faulty and fault-free classes.

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