In SwReliab/gof4srm: Goodness-of-fit tests for NHPP-based software reliability models

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gof4srm

gof4srm provides Kolmogorov-Smirnov test for both time and grouped data, and EIC (extended information criterion) for software reliability growth models.

Installation

You can install gof4srm from GitHub with:

install.packages("devtools")
devtools::install_github("SwReliab/gof4srm")

Alternatively, you can use Remote to install Rsrat from GitHub

install.packages("remotes")
remotes::install_github("SwReliab/gof4srm")

Example 1

This is an example of the estimation of software reliability growth models from a fault data (sys1g).

### load library
library(gof4srm)

### load example data
data(dacs)

### grouped data from DACS
sys1g

### Esimate all models
(result <- fit.srm.nhpp(fault=sys1g, selection=NULL))

### Draw the graph 
mvfplot(fault=sys1g, srms=result)

### Draw the graph (dmvf)
dmvfplot(fault=sys1g, srms=result)

Perform KS test with the significance level 0.05.

### KS test
ksres <- lapply(result, ks.srm.test)

### Table for P-value
pvalue <- sapply(ksres, function(x) x$p.value)
data.frame(p.value=pvalue, reject0.05=ifelse(pvalue < 0.05, "*", ""))

### MVF for the models that are passed
mvfplot(fault=sys1g, srms=result[pvalue >= 0.05])

### MVF for the models that are passed
dmvfplot(fault=sys1g, srms=result[pvalue >= 0.05])

Compute EIC

### EIC
eicres <- lapply(result, eic.srm)

### Table for EIC
eic <- sapply(eicres, function(x) x$eic)
eic.lower <- sapply(eicres, function(x) x$eic.lower)
eic.upper <- sapply(eicres, function(x) x$eic.upper)
aic <- sapply(result, function(x) x$aic)
data.frame(aic=aic, eic=eic, eic.lower=eic.lower, eic.upper=eic.upper, ks.p.value=pvalue)

Example 2 (Phase-Type SRGM)

This is an example of the estimation of software reliability growth models from a fault data (sys1g).

### Install Rphsrm
install.packages("remotes")
remotes::install_github("SwReliab/Rphsrm")

### load library
library(Rphsrm)

### Esimate several phases
(result <- fit.srm.cph(fault=sys1g, phase=c(10,20,30,40,50), selection=NULL))

### Draw the graph 
mvfplot(fault=sys1g, srms=result)

### Draw the graph (dmvf)
dmvfplot(fault=sys1g, srms=result)

Perform KS test with the significance level 0.05.

### KS test
ksres <- lapply(result, ks.srm.test)

### Table for P-value
pvalue <- sapply(ksres, function(x) x$p.value)
data.frame(p.value=pvalue, reject0.05=ifelse(pvalue < 0.05, "*", ""))

### MVF for the models that are passed
mvfplot(fault=sys1g, srms=result[pvalue >= 0.05])

### MVF for the models that are passed
dmvfplot(fault=sys1g, srms=result[pvalue >= 0.05])

Compute EIC

### EIC
eicres <- lapply(result, eic.srm)

### Table for EIC
eic <- sapply(eicres, function(x) x$eic)
eic.lower <- sapply(eicres, function(x) x$eic.lower)
eic.upper <- sapply(eicres, function(x) x$eic.upper)
aic <- sapply(result, function(x) x$aic)
data.frame(aic=aic, eic=eic, eic.lower=eic.lower, eic.upper=eic.upper, ks.p.value=pvalue)

SwReliab/gof4srm documentation built on Dec. 18, 2021, 3:05 p.m.