A Detailed Study of NHPP Software Reliability Models (Invited Paper)

Software reliability deals with the probability that software will not cause the failure of a system for a specified time under a specified condition. The probability is a function of the inputs to and use of the system as well as a function of the existing faults in the software. The inputs to the system determine whether existing faults, if any, are encountered. Software Reliability Models (SRMs) provide a yardstick to predict future failure behavior from known or assumed characteristics of the software, such as past failure data. Different types of SRMs are used for different phases of the software development life-cycle. With the increasing demand to deliver quality software, software development organizations need to manage quality achievement and assessment. While testing a piece of software, it is often assumed that the correction of errors does not introduce any new errors and the reliability of the software increases as bugs are uncovered and then fixed. The models used during the testing phase are called Software Reliability Growth Models (SRGM). Unfortunately, in industrial practice, it is difficult to decide the time for software release. An important step towards remediation of this problem lies in the ability to manage the testing resources efficiently and affordably. This paper presents a detailed study of existing SRMs based on Non-Homogeneous Poisson Process (NHPP), which claim to improve software quality through effective detection of software faults.

[1]  Swapna S. Gokhale,et al.  Effect of repair policies on software reliability , 1997, Proceedings of COMPASS '97: 12th Annual Conference on Computer Assurance.

[2]  Xuemei Zhang,et al.  Some successful approaches to software reliability modeling in industry , 2005, J. Syst. Softw..

[3]  Norman F. Schneidewind,et al.  Modelling the fault correction process , 2001, Proceedings 12th International Symposium on Software Reliability Engineering.

[4]  Santosh Kumar,et al.  Software-reliability growth model: primary-failures generate secondary-faults under imperfect debugging , 1994 .

[5]  Norman F. Schneidewind,et al.  An integrated failure detection and fault correction model , 2002, International Conference on Software Maintenance, 2002. Proceedings..

[6]  Reader Aids,et al.  Estimating Defects in Commercial , 1993 .

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

[8]  P. K. Kapur,et al.  A general discrete software reliability growth model , 1998 .

[9]  Richard G. Hamlet.  Are we testing for true reliability? , 1992, IEEE Software.

[10]  Shigeru Yamada,et al.  s-Shaped Software Reliability Growth Models and Their Applications , 1984, IEEE Transactions on Reliability.

[11]  P. K. Kapur,et al.  A software reliability growth model for an error-removal phenomenon , 1992, Softw. Eng. J..

[12]  Mitsuru Ohba,et al.  Inflection S-Shaped Software Reliability Growth Model , 1984 .

[13]  Shigeru Yamada,et al.  Software reliability assessment for an exponential-S-shaped reliability growth phenomenon , 1992 .

[14]  John D. Musa,et al.  Software reliability - measurement, prediction, application , 1987, McGraw-Hill series in software engineering and technology.

[15]  P. K. Kapur,et al.  Optimal sofware release policies for software reliability growth models under imperfect debugging , 1990 .

[16]  George J. Schick,et al.  An Analysis of Competing Software Reliability Models , 1978, IEEE Transactions on Software Engineering.

[17]  William Farr,et al.  Software reliability modeling survey , 1996 .

[18]  Hoang Pham,et al.  Handbook of reliability engineering , 2013 .

[19]  Shigeru Yamada,et al.  A software reliability growth model with two types of errors , 1985 .

[20]  K Okumoto,et al.  TIME-DEPENDENT ERROR-DETECTION RATE MODEL FOR SOFTWARE AND OTHER PERFORMANCE MEASURES , 1979 .

[21]  Ming Zhao,et al.  ON SOME RELIABILITY GROWTH MODELS WITH SIMPLE GRAPHICAL INTERPRETATIONS , 1993 .

[22]  Swapna S. Gokhale,et al.  Analysis of Software Fault Removal Policies Using a Non-Homogeneous Continuous Time Markov Chain , 2004, Software Quality Journal.

[23]  Norman F. Schneidewind,et al.  Software Reliability Model with Optimal Selection of Failure Data , 1993, IEEE Trans. Software Eng..

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

[25]  Swapna S. Gokhale,et al.  Incorporating fault debugging activities into software reliability models: a simulation approach , 2006, IEEE Transactions on Reliability.

[26]  Alan Wood,et al.  Software reliability growth models: assumptions vs. reality , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.

[27]  P. S. Grover,et al.  An S-shaped software reliability growth model with two types of errors , 1990 .

[28]  Jason Brown,et al.  Reliability: Probabilistic Models and Statistical Methods , 1996 .

[29]  Mitsuru Ohba,et al.  Software Reliability Analysis Models , 1984, IBM J. Res. Dev..

[30]  Norman F. Schneidewind.  Assessing reliability risk using fault correction profiles , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..

[31]  John D. Musa,et al.  A theory of software reliability and its application , 1975, IEEE Transactions on Software Engineering.

[32]  Bev Littleword,et al.  Forecasting Software Reliability , 1987 .

[33]  Siddhartha R. Dalal,et al.  Some Graphical Aids for Deciding When to Stop Testing Software , 1990, IEEE J. Sel. Areas Commun..

[34]  Muhammad Irfan Ullah,et al.  An approach for software reliability model selection , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..

[35]  P. C. Jha,et al.  Software Reliability Growth Models , 2011 .

[36]  P. K. Kapur,et al.  A discrete software reliability growth model with testing effort , 1994, Proceedings of 1994 1st International Conference on Software Testing, Reliability and Quality Assurance (STRQA'94).

[37]  Robert E. Mullen,et al.  The lognormal distribution of software failure rates: origin and evidence , 1998, Proceedings Ninth International Symposium on Software Reliability Engineering (Cat. No.98TB100257).

[38]  S. Kumar,et al.  Contributions to Hardware and Software Reliability , 1999, Series on Quality, Reliability and Engineering Statistics.

[39]  Amrit L. Goel,et al.  Software Reliability Models: Assumptions, Limitations, and Applicability , 1985, IEEE Transactions on Software Engineering.

[40]  Norman F. Schneidewind,et al.  Analysis of error processes in computer software , 1975, Reliable Software.

[41]  Swapna S. Gokhale,et al.  A time/structure based software reliability model , 1999, Ann. Softw. Eng..

[42]  Shigeru Yamada,et al.  S-Shaped Reliability Growth Modeling for Software Error Detection , 1983, IEEE Transactions on Reliability.

[43]  Nalina Suresh,et al.  A nonhomogeneous Poisson process for software reliability , 1996 .

[44]  Bev Littleword,et al.  Forecasting Software Reliability , 1987, Software Reliability Modelling and Identification.

[45]  Dong Tang,et al.  Quantitative reliability and availability assessment for critical systems including software , 1997, Proceedings of COMPASS '97: 12th Annual Conference on Computer Assurance.

[46]  J. G. Shanthikumar,et al.  Software reliability models: A review , 1983 .

[47]  Shunji Osaki,et al.  Discrete software reliability growth models , 1985 .

[48]  James A. Whittaker,et al.  What is software testing? And why is it so hard? , 2000 .

[49]  Peter G. Bishop,et al.  Using a log-normal failure rate distribution for worst case bound reliability prediction , 2003, 14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003..