Top > JACIII > MASP – An Enhanced Model of Fault Type Identification in ...

single-jc.php

JACIII Vol.10 No.3 pp. 312-322
doi: 10.20965/jaciii.2006.p0312
(2006)

Paper:

Views over last 60 days: 463

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
Published:
May 20, 2006
Creative Commons License
Keywords:
software fault, predictive model, neural networks, fault metrics, fault prediction and identification
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.
Cite this article as:
A. Mahaweerawat, P. Sophatsathit, C. Lursinsap, and P. Musilek, “MASP – An Enhanced Model of Fault Type Identification in Object-Oriented Software Engineering,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.3, pp. 312-322, 2006.
Data files:
References
  1. [1] “Understand for C++,” Scientific Toolworks, Inc., St. George, Utah,
    http://www.scitools.com.
  2. [2] R. T. Alexander, J. Offutt, and J. M. Bieman, “Fault Detection Capabilities of Coupling-based OO Testing,” In Proceedings of the 13th International Symposium on Software Reliability Engineering (ISSRE’02), pp. 207-218, November, 2002.
  3. [3] R. T. Alexander, J. Offutt, and J. M. Bieman, “Syntactic Fault Patterns in OO Programs,” In Proceedings of the Eight International Conference on Engineering of Complex Computer Software, pp. 193-202, December, 2002.
  4. [4] S. H. Aljahdali, A. Sheta, and D. Rine, “Prediction of Software Reliability: A Comparison between Regression and Neural Network Non-Parametric Models,” In Proceedings of IEEE International Conference on Computer Systems and Applications (AICCSA’01), pp. 470-473, June, 2001.
  5. [5] L. Briand, J. Wüst, and J. W. Daly, “Exploring the Relationships between Design Measures and Software Quality in Object-Oriented Systems,” Journal of Systems and Software, 51, pp. 245-273, 2000.
  6. [6] L. C. Briand, J. Wüst, and J. W. Daly, “Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects,” IEEE Transactions on Software Engineering, 28(7), pp. 706-720, 2002.
  7. [7] S. R. Chidamber, and C. F. Kemerer, “A Metrics Suite for Object Oriented Design,” IEEE Transactions on Software Engineering, 20(6), pp. 476-493, 1994.
  8. [8] P. Eklund, and L. Kallin, “Fuzzy Systems,” Lecture Notes prepared for courses at the Department of Computing Science at Umeå University, Sweden, February, 2000.
  9. [9] L. Emam, J.Wüst, and J. W. Daly, “The Prediction of Faulty classes Using Object-Oriented Design Metrics,” Journal of Systems and Software, 56, pp. 63-75, 2001.
  10. [10] F. Fioravanti, and P. Nesi, “A study on fault-proneness detection of Object-Oriented systems,” In Proceedings of the fifth Conference on Software Maintenance and Reengineering (CSMR’01), pp. 121-130, March, 2001.
  11. [11] D. Glasberg, and K. E. Emam, “Validating Object-Oriented Design Metrics on a Commercial Java Application,” Technical Report NRC/ERB-1080, September, 2000.
  12. [12] S. Haykin, “Neural Networks,” Prentice Hall, the United States of America, 1999.
  13. [13] T. Kamiya, S. Kusumoto, and K. Inoue, “Prediction of Fault-Proneness at Early Phase in Object-Oriented Development,” In Proceedings of the Second IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, pp. 253-258, May, 1999.
  14. [14] P. Kokol, V. Podgorelec, M. Zorman, and M. Šprogar, “An Analysis of Software Correctness Prediction Methods,” In Proceedings of the Second Asia-Pacific Conference on Quality Software (APAQS’01), pp. 33-39, December, 2001.
  15. [15] F. Lanubile, “Evaluating Predictive Models Derived From Software Measure,” Journal of Systems and Software, 38(1), pp. 225-234, 1996.
  16. [16] A. Mahaweerawat, P. Sophatsathit, and C. Lursinsap, “Software Fault Prediction Using Fuzzy Clustering and Radial-Basis Function Network,” In Proceedings of the International Conference on Intelligent Technologies, InTech/VJFuzzy’2002, pp. 304-313, December, 2002.
  17. [17] Y. Mao, H. A. Sahraoui, and H. Lounis, “Reusability Hypothesis Verification Using Machine Learning Techniques: a case study,” In Proceedings of the 13th IEEE International Conference on Automated Software Engineering, pp. 84-93, October, 1998.
  18. [18] P. Mitra, C. Murthy, and S. K. Pal, “Unsupervised Feature Selection Using Feature Similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3), pp. 301-312, 2002.
  19. [19] J. D. Musa, A. Iannino, and K. Okumoto, “Software Reliability Measurement, Prediction, Application,” McGraw-Hill Book Company, the United States of America, 1987.
  20. [20] J. Offutt, and R. Alexander, “A Fault Model for Subtype Inheritance and Polymorphism,” In 12th International Symposium on Software Reliability Engineering, pp. 84-95, November, 2001.
  21. [21] M. M. T. Thwin, and T.-S. Quah, “Application of Neural Network for Predicting Software Development Faults Using Object-Oriented Design Metrics,” In Proceedings of the 9th International Conference on Neural Information Processing, pp. 2312-2316, November, 2002.
  22. [22] P. Yu, and T. Systa, “Predicting Fault-Proneness using OO Metrics An Industrial Case Study,” In Proceedings of the Sixth European Conference on Software Maintenance and Reengineering (CSMR’02), pp. 99-107, March, 2002.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024