This Article 
 Bibliographic References 
 Add to: 
A Model of Code Sharing for Estimating Software Failure on Demand Probabilities
September 1995 (vol. 21 no. 9)
pp. 747-753
A statistical software testing model is proposed in which white box factors have a role. The model combines test adequacy notions with statistical analysis, and in so doing provides a rudimentary treatment of dependencies between test results caused by the execution of common code during the tests. The model is used to estimate the probability of failure on demand for software performing safety shutdown functions on large plants and concerns the case where extensive test results are available on the latest version of the software, none of which have resulted in software failure. According to the model, there are circumstances in which some current statistical models for dynamic software testing are too conservative, and others are not conservative, depending on the software architecture.

[1] R.W. Butler and G.B. Finelli,“The infeasibility of quantifying the reliability of life-critical real-timesoftware,” IEEE Trans. Software Engineering, vol. 19, no. 1, pp. 3-12, 1993.
[2] G. Casella and R.L. Berger,Statistical Inference.Pacific Grove, Calif.: Wadsworth&Brooks/Cole, 1990.
[3] S.C. Duran and J.W. Ntafos,“An evaluation of random testing,” IEEE Trans. Software Engineering, vol. 10, no. 4, pp. 438-444, 1984.
[4] W. Ehrenburger,“Probabilistic techniques for software verification in safety applicationsof computerised process control in nuclear power plants,” IAEA-TECDOC-581, Feb. 1991
[5] J.E. Freund and R.L. Walpole,Mathematical Statistics.Englewood Cliffs, N.J.: Prentice-Hall, 1980.
[6] R.G. Hamlet, “Probable Correctness Theory,” Information Processing Letters, vol. 25, pp. 17–25, Apr. 1987.
[7] D. Hamlet and R. Taylor, "Partition Testing Does Not Inspire Confidence," IEEE Trans. Software Eng., vol. 16, pp. 1,402-1,412, Dec. 1990.
[8] R.G. Hamlet,“Are we testing true reliability?” IEEE Software, pp. 21-27, July 1992.
[9] International Electrotechnical Commission, Software for Computers in the Application of Industrial Safety-RelatedSystems, SC65A/WG9 Draft Document (IEC reference 65A Secretariet 122), Nov. 1991
[10] J.-C. Laprie and K. Kanoun,“X-ware reliability and availability modelling” IEEE Trans. Software Engineering, vol. 18, no. 2, pp. 130-147, 1992.
[11] Lees and Ang, eds., Safety Cases. Butterworth, 1989.
[12] B. Littlewood,“Software reliability models for modular program structure” IEEE Trans. on Reliability, vol. 30, pp. 313-320, Oct. 1981.
[13] B. Littlewood, and L. Strigini,“Validation of ultra-high dependability for software-based systems,” Comm. ACM, vol. 36, no. 11, pp. 69-80, Nov. 1993.
[14] J.H.R. May and A.D. Lunn,“New statistics for demand-based software testing,” Information Processing Letters, vol. 53, pp. 307-314, 1995.
[15] W.M. Miller,L.J. Morell,R.E. Noonan,S.K. Park,D.M. Nicol,B.W. Murrill,, and J.M. Voas,“Estimating the probability of failure when testing reveals nofailures,” IEEE Trans. Software Engineering, vol. 18, no. 1, pp. 33-43, 1992.
[16] H.D. Mills,M. Dyer,, and R.C. Linger,“Cleanroom software engineering,” IEEE Software, pp. 19-25, Sept. 1987.
[17] Ministry of Defence Directorate of Standardisation (UK), Interim Defence Standard 00-55: The Procurement of Safety Critical Softwarein Defence Equipment, Parts 1-2, 1991.
[18] J.D. Musa,A. Iannino,, and K. Okumoto,Software Reliability: Measurement, Prediction and Application.New York: McGraw-Hill, 1987.
[19] G.J. Myers,The Art of Software Testing.New York: Wiley, 1979.
[20] J.H. Poore,H.D. Mills,, and D. Mutcher,“Planning and certifying software system reliability,” IEEE Software, pp. 88-99, Jan. 1993.
[21] J.A. Rice,Mathematical Statistics and Data Analysis, 2nd ed., Wadsworth, 1995.
[22] E.J. Weyuker and B. Jeng,“Analyzing partition testing strategies,” IEEE Trans. Software Engineering, vol. 17, pp. 703-711, 1991.

Index Terms:
Software failure on demand, probability of failure on demand, probability model, statistical estimation, code sharing, demand space partitioning, probabilistic dependence assumptions.
J.h.r. May, A.d. Lunn, "A Model of Code Sharing for Estimating Software Failure on Demand Probabilities," IEEE Transactions on Software Engineering, vol. 21, no. 9, pp. 747-753, Sept. 1995, doi:10.1109/32.464546
Usage of this product signifies your acceptance of the Terms of Use.