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ABSTRACT

In any manufacturing environment, the fault introduction rate might be considered one of the most meaningful criterion to evaluate the goodness of the development process. In many investigations, the estimates of such a rate are often oversimplified or misunderstood generating unrealistic expectations on the prediction power of regression models with a fault criterion. The computation of fault introduction rates in software development requires accurate and consistent measurement, which translates into demanding parallel efforts for the development organization. This paper presents the techniques and mechanisms that can be implemented in a software development organization to provide a consistent method of anticipating fault content and structural evolution across multiple projects over time. The initial estimates of fault introduction rates can serve as a baseline against which future projects can be compared to determine whether progress is being made in reducing the fault introduction rate, and to identify those development techniques that seem to provide the greatest reduction.

REFERENCES

• 1: Ram Chillarege , Inderpal S. Bhandari , Jarir K. Chaar , Michael J. Halliday , Diane S. Moebus , Bonnie K. Ray , Man-Yuen Wong, Orthogonal Defect Classification-A Concept for In-Process Measurements, IEEE Transactions on Software Engineering, v.18 n.11, p.943-956, November 1992 [doi>10.1109/32.177364]
• 2: Elbaum, S. G., and Munson, J. C. 1998. A standard for the measurement of C complexity attributes. Technical report: TR-CS-98-02, Software Engineering Testing Lab, University of Idaho.
• 3: J. C. Munson , S. G. Elbaum, Code Churn: A Measure for Estimating the Impact of Code Change, Proceedings of the International Conference on Software Maintenance, p.24, March 16-19, 1998
• 4: N. Fenton, Software Measurement: A Necessary Scientific Basis, IEEE Transactions on Software Engineering, v.20 n.3, p.199-206, March 1994 [doi>10.1109/32.268921]
• 5: IEEE Standard Dictionary of Measures to Produce Reliable Software. 1989. IEEE Std. 982.1. Institute of Electrical and Electronics Engineers.
• 6: IEEE Standard Classification for Software Anomalies. 1993. IEEE Std. 1044. Institute of Electrical and Electronics Engineers.
• 7: Khoshgoftaar, T. M., and Munson, J. C. 1990. Predicting software development errors using complexity metrics. Journal on Selected Areas in Communications 8: 253-261.
• 8: Khoshgoftaar, T. M., and Munson, J. C. 1992. A measure of software system complexity and its relationship to faults. Proceedings of the International Simulation Technology Conference San Diego, CA, 267-272.
• 9: Luqi, A Graph Model for Software Evolution, IEEE Transactions on Software Engineering, v.16 n.8, p.917-927, August 1990 [doi>10.1109/32.57627]
• 10: Munson, J. C., and Khoshgoftaar, T. M. 1990. Regression modeling of software quality: An empirical investigation. Journal of Information and Software Technology 32: 105-114.
• 11: Munson, J. C., and Khoshgoftaar, T. M. 1990. The relative software complexity metric: A validation study. Proceedings of the Software Engineering Conference. Cambridge, UK, 89-102.
• 12: John C. Munson , Taghi M. Khoshgoftaar, The Detection of Fault-Prone Programs, IEEE Transactions on Software Engineering, v.18 n.5, p.423-433, May 1992 [doi>10.1109/32.135775]
• 13: Munson, J. C. 1995. Software measurement: problems and practice. Annals of Software Engineering J. C. Baltzer AG, (1): 255-285.
• 14: Munson, J. C. 1996. Software faults, software failures, and software reliability modeling. Information and Software Technology N38: 687-699.
• 15: J. C. Munson , D. S. Werries, Measuring software evolution, Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results, p.41, March 25-26, 1996
• 16: IEEE Software Staff, Faults, Failures, and a Metrics Revolution, IEEE Software, v.6 n.2, p.85, March 1989
• 17: Allen Peter Nikora , Ellis Horowitz, Software system defect content prediction from development process and product characteristics, University of Southern California, Los Angeles, CA, 1998
• 18: Nikora, A. P., Schneidewind, N. F., Munson, J. C. 1997. V&V issues in achieving high reliability and safety in critical control system software. Proceedings of the International Society of Science and Applied Technological Conference, Anaheim, California, 25-30.
• 19: Nikora, A. P., and Munson, J. C 1998. Software evolution and the fault process. Proceedings of the twenty third annual software engineering workshop, NASA/Goddard Space Flight Center (GSFC) Software Engineering Laboratory (SEL).
• 20: A. Nikora , J. Munson, Determining Fault Insertion Rates for Evolving Software Systems, Proceedings of the The Ninth International Symposium on Software Reliability Engineering, p.306, November 04-07, 1998

INDEX TERMS

Keywords:
Software evolution, fault introduction process, software measurement