The L p -norm estimation of the parameters for the Jelinski–Moranda model in software reliability

The exponential model of Jelinski and Moranda [Software reliability research, in Statistical Computer Performance Evaluation, W. Freiberg, ed., Academic Press, New York, 1972, pp. 465–484] is one of the earliest models proposed for predicting software reliability. The estimation of its parameters has been approached in the literature by various techniques. The focus of this paper is on the L p -norm (1≤p<∞) fitting approach. Special attention is paid to the nonlinear weighted least squares (LS) estimation. We show that it is possible for the best L p -norm estimate to not exist. As the main result, a necessary and sufficient condition for the existence of the best L p -norm estimate is obtained. This condition is theoretical in nature. We apply it to obtain two theorems on the existence of the LS estimate. One of them gives the necessary and sufficient conditions which guarantee the existence of the LS estimate. To illustrate the problems arising with the nonlinear normal equation approach for solving the LS problem, some illustrative examples are included.

[1]  Norman F. Schneidewind,et al.  A Quantitative Approach to Software Development Using IEEE 982.1 , 2007, IEEE Software.

[2]  Min Xie,et al.  Software Reliability Modelling , 1991, Series on Quality, Reliability and Engineering Statistics.

[3]  Bev Littlewood,et al.  A Bayesian Reliability Growth Model for Computer Software , 1973 .

[4]  Hoang Pham,et al.  System Software Reliability , 1999 .

[5]  Kai-Yuan Cai,et al.  On estimating the number of defects remaining in software , 1998, J. Syst. Softw..

[6]  D. Jukic,et al.  On nonlinear weighted total least squares parameter estimation problem for the three-parameter Weibull density , 2010 .

[7]  H. N. Nagaraja,et al.  Order Statistics, Third Edition , 2005, Wiley Series in Probability and Statistics.

[8]  Dragan Jukic On the existence of the best discrete approximation in lp norm by reciprocals of real polynomials , 2009, J. Approx. Theory.

[9]  Saeed Ghahramani Fundamentals of Probability , 1995 .

[10]  Norman F. Schneidewind,et al.  Applying reliability models to the space shuttle , 1992, IEEE Software.

[11]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[12]  Jacob Tal Development and Evaluation of Software Reliability Estimators , 1976 .

[13]  Shigeruyamada Software Reliability Models and Their Applications: A Survey ShigeruYamada† , 2022 .

[14]  P. Carnes Software reliability in weapon systems , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering - Case Studies -.

[15]  Simon P. Wilson,et al.  Nonparametric Analysis of the Order-Statistic Model in Software Reliability , 2007, IEEE Transactions on Software Engineering.

[16]  Michiel van Genuchten,et al.  Using Software Reliability Growth Models in Practice , 2007, IEEE Software.

[17]  G. A. Watson,et al.  Use of lp norms in fitting curves and surfaces to data , 2004 .

[18]  Douglas R. Miller Exponential order statistic models of software reliability growth , 1986, IEEE Transactions on Software Engineering.

[19]  Z. Jelinski,et al.  Software reliability Research , 1972, Statistical Computer Performance Evaluation.

[20]  Hoang Pham,et al.  System Software Reliability (Springer Series in Reliability Engineering) , 2007 .

[21]  Nozer D. Singpurwalla,et al.  A Unification of Some Software Reliability Models , 1985 .

[22]  R. Jackson Inequalities , 2007, Algebra for Parents.

[23]  Rudolf Scitovski,et al.  Existence of optimal solution for exponential model by least squares , 1997 .

[24]  G. Seber,et al.  Nonlinear Regression: Seber/Nonlinear Regression , 2005 .

[25]  Karama Kanoun,et al.  A Method for Software Reliability Analysis and Prediction Application to the TROPICO-R Switching System , 1991, IEEE Trans. Software Eng..

[26]  Anirban DasGupta,et al.  Fundamentals of Probability: A First Course , 2010 .

[27]  Herbert A. David,et al.  Order Statistics , 2011, International Encyclopedia of Statistical Science.

[28]  Andrej Pázman,et al.  Nonlinear Regression , 2019, Handbook of Regression Analysis With Applications in R.

[29]  Eugene Demidenko,et al.  Criteria for global minimum of sum of squares in nonlinear regression , 2006, Comput. Stat. Data Anal..

[30]  Dragan Jukic,et al.  Total least squares fitting Bass diffusion model , 2011, Math. Comput. Model..

[31]  E. Demidenko,et al.  Criteria for Unconstrained Global Optimization , 2008 .

[32]  Kristian Sabo,et al.  Least‐squares problems for Michaelis–Menten kinetics , 2007 .

[33]  John D. Musa,et al.  Software Reliability Engineering: More Reliable Software Faster and Cheaper , 2004 .