Open AccessStochastic Formulation of Fault Severity Based Multi Release SRGM Using the Effect of Logistic Learning
Author(s) -
Jayanti Singh,
Suneeta Bhati,
A. R. Prasanan,
Ashok Vayas
Publication year - 2017
Publication title -
international journal of mathematical, engineering and management sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 10
ISSN - 2455-7749
DOI - 10.33889/ijmems.2017.2.3-015
Subject(s) - computer science , software quality , reliability (semiconductor) , debugging , software , reliability engineering , process (computing) , fault (geology) , software reliability testing , stage (stratigraphy) , real time computing , software development , engineering , operating system , seismology , geology , paleontology , power (physics) , physics , quantum mechanics , biology
In today’s environment, software reliability is one of the major concerns for Software firms. Many Software Reliability Growth Model (SRGM) has been developed and many are under process. In order to meet the requirements of consumer and to excel in competitive environment, companies are coming up with multiple add –ons. We design the model as stochastic with continuous state space because of large software system, the count of failures observed is huge and so, the variation in count of errors detected/ removed in each debugging is petite compared to original error content at the beginning of testing. This study is an add on to the software reliability literature where we have developed multi release SRGM’s based on available concept of depending on previous releases. The errors have been categorically divided upon the severity of their removal as one stage, two stage, three stage fault removal process is applied in an environment of irregular fluctuations.