Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study

• Basili, V. R., Briand, L. C., and Melo, W. L. 1996. A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering 22(10): 751–761, October.

  Google Scholar 

• Beaumont, G. P. 1996. Statistical Tests: An Introduction with Minitab Commentary. Prentice Hall.

• Berenson, M. L., Levine, D. M., and Goldstein, M. 1983. Intermediate Statistical Methods and Applications: A Computer Package Approach. Englewood Cliffs, NJ: Prentice Hall, USA.

  Google Scholar 

• Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1984. Classification And Regression Trees. Belmont, California, USA: Wadsworth International Group, 2nd edition.

  Google Scholar 

• Briand, L. C., Basili, V. R., and Hetmanski, C. J. 1993. Developing interpretable models with optimized set reduction for identifying high-risk software components. IEEE Transactions on Software Engineering 19(11): 1028–1044, November.

  Google Scholar 

• Briand, L. C., Melo, W. L., and Wust, J. 2002. Assessing the applicability of fault-proneness models across object-oriented software projects. IEEE Transactions on Software Engineering 28(7): 706–720.

  Google Scholar 

• Clark, L. A., and Pregibon, D. 1992. Tree-based models. In J. M. Chambers and T. J. Hastie, (eds), Statistical Models in S. Wadsworth International Group, Pacific Grove, California, pp. 377–419.

  Google Scholar 

• Ebert, C. 1996. Classification techniques for metric-based software development. Software Quality Journal 5(4): 255–272.

  Google Scholar 

• Fayyad, U. M. 1996. Data mining and knowledge discovery: Making sense out of data. IEEE Expert 11(4): 20–25.

  Google Scholar 

• Johnson, R. A., and Wichern, D. W. 1992. Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ: Prentice Hall, USA, 2nd edition.

  Google Scholar 

• Khoshgoftaar, T. M., and Allen, E. B. 1999. Logistic regression modeling of software quality. International Journal of Reliability, Quality and Safety Engineering 6(4): 303–317.

  Google Scholar 

• Khoshgoftaar, T. M., and Allen, E. B. 2000. A practical classification rule for software quality models. IEEE Transactions on Reliability 49(2): 209–216.

  Google Scholar 

• Khoshgoftaar, T. M., and Allen, E. B. 2001. Controlling overfitting in classification-tree models of software quality. Empirical Software Engineering 6(1): 59–79.

  Google Scholar 

• Khoshgoftaar, T. M., Allen, E. B., and Deng, J. 2002. Using regression trees to classify fault-prone software modules. IEEE Transactions on Reliability 51(4): 455–462.

  Google Scholar 

• Khoshgoftaar, T. M., Allen, E. B., Hudepohl, J. P., and Aud, S. J. 1997. Applications of neural networks to software quality modeling of a very large telecommunications system. IEEE Transactions on Neural Networks 8(4): 902–909.

  Google Scholar 

• Khoshgoftaar, T. M., Allen, E. B., Jones, W. D., and Hudepohl, J. P. 2000. Classification tree models of software-quality over multiple releases. IEEE Transactions on Reliability 49(1): 4–11.

  Google Scholar 

• Khoshgoftaar, T. M., and Seliya, N. 2002. Software quality classification modeling using the SPRINT decision tree algorithm. In Proceedings: 14th International Conference on Tools with Artificial Intelligence. Washington, DC, USA: IEEE Computer Society, November, pp. 365–374.

  Google Scholar 

• Kolodner, J. 1993. Case-Based Reasoning. San Mateo, California USA: Morgan Kaufmann Publishers, Inc.

  Google Scholar 

• Leake, D. B. (ed.) 1996. Case-Based Reasoning: Experience, Lessons, and Future Directions. Cambridge, MA USA: MIT Press.

  Google Scholar 

• Mehta, M., Agarwal, R., and Rissanen, J. 1996. SLIQ: A fast scalable classifier for data mining. IBM White Paper Series: Available at www.almaden.ibm.com/cs/quest, March.

• Mehta, M., Rissanen, J., and Agarwal, R. 1995. MDL-based decision tree pruning. IBM White Paper Series: Available at www.almaden.ibm.com/cs/quest, August.

• Myers, R. H. 1990. Classical and Modern Regression with Applications. PWS-KENT, Boston, MA, USA.

  Google Scholar 

• Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. 1996. Applied Linear Statistical Models. 4th edition. Chicago, IL, USA: Irwin McGraw-Hill.

  Google Scholar 

• Ohlsson, M. C., Helander, M., and Wohlin, C. 1996. Quality improvement by identification of fault-prone modules using software design metrics. In Proceedings: International Conference on Software Quality, Ottawa, Ontario, Canada, pp. 1-13.

• Ohlsson, M. C., and Runeson, P. 2002. Experience from replicating empirical studies on prediction models. In Proceedings: 8th International Software Metrics Symposium. Ottawa, Ontario, Canada: IEEE Computer Society, June, pp. 217–226.

  Google Scholar 

• Ohlsson, N., Zhao, M., and Helander, M. 1998. Application of multivariate analysis for software fault prediction. Software Quality Journal 7(1): 51–66.

  Google Scholar 

• Paul, R. 1992. Metric-based neural network classification tool for analyzing large-scale software. In Proceedings: International Conference on Tools with Artificial Intelligence. Arlington, Virginia, USA: IEEE Computer Society, November, pp. 108–113.

  Google Scholar 

• Ping, Y., Systa, T., and Muller, H. 2002. Predicting fault-proneness using OO metrics, an industrial case study. In T. Gyimothy and F. B. Abreu (eds), Proceedings: 6th European Conference on Software Maintenance and Reengineering. Budapest, Hungary, March, pp. 99-107.

• Pizzi, N. J., Summers, A. R., and Pedrycz, W. 2002. Software quality prediction using median-adjusted class labels. In Proceedings: International Joint Conference on Neural Networks, volume 3. Honolulu, Hawaii, USA: IEEE Computer Society, May, pp. 2405–2409.

  Google Scholar 

• Ponnuswamy, V. 2001. Classification of software quality with tree modeling using C4.5 algorithm. Master's thesis, Florida Atlantic University, Boca Raton, Florida USA, December. Advised by Taghi M. Khoshgoftaar.

  Google Scholar 

• Quinlan, J. R. 1993. Machine Learning, C4.5: Programs For Machine Learning. San Mateo, California: Morgan Kaufmann.

  Google Scholar 

• Ross, F. D. 2001. An empirical study of analogy based software quality classification models. Master's thesis, Florida Atlantic University, Boca Raton, FL USA, August. Advised by T. M. Khoshgoftaar.

  Google Scholar 

• Schneidewind, N. F. 1995. Software metrics validation: Space shuttle flight software example. Annals of Software Engineering 1: 287–309.

  Google Scholar 

• Schneidewind, N. F. 2001. Investigation of logistic regression as a discriminant of software quality. In Proceedings: 7th International Software Metrics Symposium. London UK: IEEE Computer Society, April, pp. 328–337.

  Google Scholar 

• Shafer, J., Agarwal, R., and Mehta, M. 1996. SPRINT: A scalable parallel classifier for data mining. IBM White Paper Series: Available at www.almaden.ibm.com/cs/quest, September.

• Shepperd, M., and Kadoda, G. 2001. Comparing software prediction techniques using simulation. IEEE Transactions on Software Engineering 27(11): 1014–1022.

  Google Scholar 

• Steinberg, D., and Colla, P. 1995. CART: A supplementary module for SYSTAT. Salford Systems, San Diego, California.

  Google Scholar 

• Suarez, A., and Lutsko, J. F. 1999. Globally optimal fuzzy decision trees for classification and regression. Pattern Analysis and Machine Intelligence 21(12): 1297–1311, IEEE Computer Society.

  Google Scholar 

• Takahashi, R., Muraoka, Y., and Nakamura, Y. 1997. Building software quality classification trees: Approach, experimentation, evaluation. In Proceedings: 8th International Symposium on Software Reliability Engineering. Albuquerque, NM, USA: IEEE Computer Society: November, pp. 222–233.

  Google Scholar 

• Votta, L. G., and Porter, A. A. 1995. Experimental software engineering: A report on the state of the art. In Proceedings of the 17th. International Conference on Software Engineering. Seattle, WA USA: IEEE Computer Society, April, pp. 277–279.

  Google Scholar 

Download references