Open AccessModeling Software Multi Up-Gradations with Error Generation and Fault Severity
Author(s) -
Ritu Verma,
R. S. Parihar,
Subhajit Das
Publication year - 2018
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.2018.3.4-030
Subject(s) - software , computer science , software development , software engineering , software metric , software construction , software fault tolerance , trace (psycholinguistics) , software quality , software sizing , reliability engineering , judgement , engineering , operating system , linguistics , philosophy , law , political science
World is moving very fast and with each new step ‘our desire to want something more’ is increasing in all the aspects of lives. Keeping in mind the growing demand of society for highly equipped software; the suppliers tend to come with up-gradations very frequently with the desire to make an impression in the market. Each organization is battling to showcase that their products is an enhanced version. As a result, companies at proper intervals are coming up with innovative functionalities. In order to have an upper hand amongst the competitors; the judgement on apt time of release of a software is utmost sensitive. This decision is majorly governed by company’s expertise in being able to remove all kinds of faults in the software. Development of a multi-release upgraded software reliability growth model is done with regard to trace the upshots of faults in line for the existing software and the one’s remaining in the software at different intervals. This model ascertains the remaining bugs occurred in the software during its operational phase while testing of the new code in other words which occurs in course of addition of new features to the current software. Further, the case of three types of faults existing in the software namely: simple, hard and complex has been incorporated to model the fault removal phenomenon. Data Analysis has been given to support the results.