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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749 . Open Access


Testing Domain Dependent Software Reliability Growth Models

Testing Domain Dependent Software Reliability Growth Models

Deepika
Department of Operational Research, University of Delhi, Delhi-110007, India.

Ompal Singh
Department of Operational Research, University of Delhi, Delhi-110007, India.

Adarsh Anand
Department of Operational Research, University of Delhi, Delhi 110007, India.

Jyotish N. P. Singh
Ramjas College, University of Delhi, Delhi-110007, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2017.2.3-013

Received on October 31, 2016
  ;
Accepted on January 01, 2017

Abstract

Software Reliability Growth Models (SRGMs) are supporting software industries in expecting and scrutinizing quality of software. Numerous SRGMs have been proposed; majority of which concentrate on testing period of software. For testing, domain specific knowledge plays a very crucial role. Based on necessity condition, a set of programmes are in testing phase of software development. “Domain testing is a software technique in which small number of test cases is selected for trial. These sets of testing paths, all of which are to be eventually influenced by designed test cases are called the testing domain which expands with the progress of testing”. Keeping this concept in mind, we propose SRGMs with the concept of testing domain with exponential coverage. Utility of proposed framework has been emphasized in this paper through some models pertaining to different distribution i.e Exponential, Logistic, Weibull and Rayleigh. Moreover, the data analysis is performed to find the estimates of parameters by fitting the models on authentic data sets.

Keywords- SRGMs, Detection rate of faults, Distribution function, Density function, Testing domain.

Citation

Deepika,Singh, O., Anand, A., & Singh, J. N. P. (2017). Testing Domain Dependent Software Reliability Growth Models. International Journal of Mathematical, Engineering and Management Sciences, 2(3), 140-149. https://dx.doi.org/10.33889/IJMEMS.2017.2.3-013.