A Software Defect Prediction Model during the Test Period

Abstract:

Effective defect prediction is an important topic in software engineering. This paper studies multiple defect prediction models and proposes a defect prediction model during the test period for organic project. This model is based on the analysis of project defect data and refer to Rayleigh model. Defect prediction model plays an important role in the analysis of software quality, rationally allocating resources of software test, improving the efficiency of software test. This paper selected representative software defect data to apply this model, which has been shown to improve project performance.

• Periodical: Applied Mechanics and Materials (Volumes 475-476)
• Pages: 1186-1189
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.475-476.1186
• Online since: December 2013
• Authors: Wan Jiang Han*, Li Xin Jiang, Xiao Yan Zhang, Yi Sun
• Keywords: Defect Distribution, Defect Prediction Model, Software Defect, Test Phase

References

[1] Lyu, Michael R., Handbook of Software Reliability Engineering, McGraw-Hill, Inc., (1996).

[2] Akiyama F. An example of software system debugging. In: Proc. Of the Int'l Federation of Information Proc. Societies Congress. New York: Spring Science and Business Media, 1971. 353-359.

[3] Halstead MH. Elements of Software Science. New York: Elsevier. North-Holland, (1977).

[4] Lipow M. Number of faults per line of code. IEEE Trans. On Software Engineering, 1982. 8(4): 437-439.

DOI: 10.1109/tse.1982.235579

[5] Takahashi M, Kamayachi Y. An empirical study of a model for program error prediction. IEEEE Trans. On Software Engineering, 1989, 15(1): 82-86.

DOI: 10.1109/32.21729

[6] Malayia Y, Denton J. Module size distribution and defect density. In: Proc. Of the 11th Int'd Symp on Software Reliability Engineering . New York: IEEE Computer Society Press, 2000. 62-71.

DOI: 10.1109/issre.2000.885861

[7] Chulani S. Bayesian analysis software cost and quality models [Ph.D. Thesis]. Los Angeles: Univcrsity of Southern California, (1999).

[8] Chulani S. Results of Delphi for the defect introduction model, sub-model of the cost/quality model extension to COCOMO II. Technical Report, USC-CSE-97-504, (1997).

[9] Chulani S, Boehm B. Modeling software defect introduction and removal: COQUALMO (COnstructive QUALity MOdel). Technical Report. USC-CSE-99-510, (1999).

[10] Chillarege R, Bhandari I, Jarik K, Michael J, Diane S, Bonnie K, Man-Yuen W. Orthogonal defect classification-a concept for in-process measurements. IEEE Trans. on Software Engineering, 1992, 18(11): 943-956.

DOI: 10.1109/32.177364

[11] Mills H. On the statistical validation of computer programs. Technical Report, FSC-72-6015, IBM Federal Systems Division, (1972).

[12] Chen HW, Wang J, Dong W. High confidence software engineering technologies. Acta Electronica Sinica, 2003, 31(12A): 1933-1938(in Chinese with English abstract).

[13] GUO SH, Lan YG, Jin MZ. Some issues about trusted components research. Computer Science, 2007, 34(5): 243-246 (in Chinese with English abstract).

[14] Boehm B. Value-Based Software engineering-overview and agenda. Technical Report, USC-CSE 2005-504, (2005).

[1] .