Multi up-gradation software reliability growth model with imperfect debugging

Due to demand of new features and highly reliable software system, the software industries are speeding their up-gradations/add-ons in the software. The life of software is very short in the environment of perfect competition. Therefore the software developers have to come up with successive up gradations to survive. The reported bugs from the existing software and Features added to the software at frequent time intervals lead to complexity in the software system and add to the number of faults in the software. The developer of the software can lose on market share if it neglects the reported bugs and up gradation in the software and on the other hand a software company can lose its name and goodwill in the market if the reported bugs and functionalities added to the software lead to an increase in the number of faults in the software. To capture the effect of faults due to existing software and generated in the software due to add-ons at various points in time, we develop a multi up-gradation, multi release software reliability model. This model uniquely identifies the faults left in the software when it is in operational phase during the testing of the new code i.e. developed while adding new features to the existing software. Due to complexity and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/detection of a failure and the original fault may remain resulting in the phenomenon known as imperfect debugging, or get replaced by another fault causing error generation The model developed is validated on real data sets with software which has been released in the market with new features four times.

[1]  Amrit L. Goel,et al.  Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures , 1979, IEEE Transactions on Reliability.

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

[3]  Yan Luo,et al.  Software Reliability Growth Modelling using aWeighted Laplace Test Statistic , 2007, 31st Annual International Computer Software and Applications Conference (COMPSAC 2007).

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

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

[6]  Sergio Bittanti Software Reliability Modelling and Identification , 1988, Lecture Notes in Computer Science.

[7]  Hoang Pham,et al.  NHPP software reliability and cost models with testing coverage , 2003, Eur. J. Oper. Res..

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

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

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

[11]  Gurjeet Kaur,et al.  Multi up-gradation software reliability model , 2010, 2010 2nd International Conference on Reliability, Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods (ICRESH).

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

[13]  Shunji Osaki,et al.  Software reliability measurement and assessment methods during operation phase and their comparisons , 1992, Systems and Computers in Japan.

[14]  Chin-Yu Huang,et al.  Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models , 2008, J. Syst. Softw..

[15]  S. Bittanti,et al.  A Flexible Modelling Approach for Software Reliability Growth , 1987, Software Reliability Modelling and Identification.