Abstract
Analyze the predict capability of some SRGMs to understand the different parameters to facilitate the estimate process. The predict validity analysis will be on two key factors, one pertaining to the degree of fitment on available failure data and the other for its prediction capability. The validity analysis will be to arrive at trade off in choosing a simple model as compared to complex model by determining their performances across multiple data sets. Data for the predict validity analysis has been taken from different time periods to understand the impact of these models across various technologies and process during the time frame.
Highlights
There are many Software Reliability Models available
Fault identification is rapid in initial stage and reaches a steady state
In this paper we focus on validating predictions of two or more parameters software reliability growth models
Summary
Each model use two are three parameters to get the reliability estimation from the actual failure data. These models are designed based on the expectation of the trend in the failure data .To identify the trends some assumptions are assumed and they are: Fault identification is rapid in initial stage and reaches a steady state. Performance capability of testing team Complexity of application domain Kind of technology used Size of the application The kind of software development process The number of faults identified by an experienced tester is more than the less experience tester. New technologies introduced changes in the software development process SSAD (Structured System analysis and Design) technique was used in early times .With the advent of distributed objects OOAD (Object Oriented Analysis and Design) technique used. Along with predictions done by Prince[1](2006), we present the Log -Logistic model for the both ungrouped and grouped data inputs taken from the early 80’s and 90’s.The degree of the fitment is studied for the loglogistic data using a specific percentage available failure data and validate the predictive performance of the model along with the models validated by Prince[1](2006)
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