README.md
In okamumu/Rsrat: A Software Reliability Assessment Tool with NHPP-Based Models
Rsrat
Rsrat provides the package to evalute the software reliability from the
fault data collected in the testing phase. Rsrat can use two types of
data; fault-detection time data and its grouped data. The
fault-detection time data is a sequence of time intervals of fault
detection times (CPU time, etc). Also its grouped data is a sequence of
the number of detected faults for each time interval (per a working day,
per a week, etc). The reliability evaluation is based on the software
reliability growth model with NHPP (non-homogeneous Poisson process).
Installation
You can install Rsrat from GitHub with:
install.packages("devtools")
devtools::install_github("SwReliab/Rsrat")
Alternatively, you can use Remote to install Rsrat from GitHub
install.packages("remotes")
remotes::install_github("SwReliab/Rsrat")
Example
This is an example of the estimation of software reliability growth
models from a fault data (tohma).
### load library
library(Rsrat)
### load example data
data(dacs)
### tohma is a grouped data
tohma
#> [1] 5 5 5 5 6 8 2 7 4 2 31 4 24 49 14 12 8 9 4 7 6 9 4 4 2
#> [26] 4 3 9 2 5 4 1 4 3 6 13 19 15 7 15 21 8 6 20 10 3 3 8 5 1
#> [51] 2 2 2 7 2 0 2 3 2 7 3 0 1 0 1 0 0 1 1 0 0 1 1 0 0
#> [76] 0 1 2 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 1
#> [101] 0 0 1 0 0 1 0 0 1 0 1
### Esimate all models and select the best one in terms of AIC
(result <- fit.srm.nhpp(fault=tohma))
#> Model name: lxvmin
#> omega loclog scalelog
#> 481.7029 -3.4642 0.6637
#> Maximum LLF: -316.2599
#> AIC: 638.5198
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=tohma, srms=result)
rateplot(fault=tohma, srms=result)
The second example illustrates the estimation for two specified models.
### All models in the package
srm.models
#> [1] "exp" "gamma" "pareto" "tnorm" "lnorm" "tlogis" "llogis" "txvmax"
#> [9] "lxvmax" "txvmin" "lxvmin"
### Estimate two models and no select
(result <- fit.srm.nhpp(fault=tohma, srm.names=c("exp", "gamma"), selection=NULL))
#> $exp
#> Model name: exp
#> omega rate
#> 497.2912 0.0308
#> Maximum LLF: -359.8777
#> AIC: 723.7555
#> Convergence: TRUE
#>
#>
#> $gamma
#> Model name: gamma
#> omega shape rate
#> 483.52301 1.88475 0.06447
#> Maximum LLF: -319.5695
#> AIC: 645.139
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=tohma, srms=result)
### Draw the graph (dmvf)
dmvfplot(fault=tohma, srms=result)
rateplot(fault=tohma, srms=result)
The third example shows the case where the fault data are fault
detection data.
### fault-detection time data
#### Time intervals for all faults
#### The last value is a negative value, that indicates the time interval in which there is no fault detection after the last fault detection.
sys1
#> [1] 3 30 113 81 115 9 2 91 112 15 138 50
#> [13] 77 24 108 88 670 120 26 114 325 55 242 68
#> [25] 422 180 10 1146 600 15 36 4 0 8 227 65
#> [37] 176 58 457 300 97 263 452 255 197 193 6 79
#> [49] 816 1351 148 21 233 134 357 193 236 31 369 748
#> [61] 0 232 330 365 1222 543 10 16 529 379 44 129
#> [73] 810 290 300 529 281 160 828 1011 445 296 1755 1064
#> [85] 1783 860 983 707 33 868 724 2323 2930 1461 843 12
#> [97] 261 1800 865 1435 30 143 108 0 3110 1247 943 700
#> [109] 875 245 729 1897 447 386 446 122 990 948 1082 22
#> [121] 75 482 5509 100 10 1071 371 790 6150 3321 1045 648
#> [133] 5485 1160 1864 4116 -2526
### Esimate
(result <- fit.srm.nhpp(time=sys1[sys1>=0], te=-sys1[sys1<0]))
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Model name: gamma
#> omega shape rate
#> 1.545e+02 6.357e-01 1.618e-05
#> Maximum LLF: -967.1074
#> AIC: 1940.215
#> Convergence: TRUE
### Draw the graph
mvfplot(time=sys1[sys1>=0], te=-sys1[sys1<0], srms=result)
The fourth example illustrates the case where the mvfs for all the
models are drawn.
### Esimate and return all the estimated results
(result <- fit.srm.nhpp(fault=sys1g, selection=NULL))
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> $exp
#> Model name: exp
#> omega rate
#> 4.295e+03 3.347e-04
#> Maximum LLF: -192.5611
#> AIC: 389.1221
#> Convergence: FALSE
#>
#>
#> $gamma
#> Model name: gamma
#> omega shape rate
#> 5.400e+02 1.799e+00 8.543e-03
#> Maximum LLF: -182.2326
#> AIC: 370.4651
#> Convergence: TRUE
#>
#>
#> $pareto
#> Model name: pareto
#> omega shape scale
#> 1179.22 56.39 44141.81
#> Maximum LLF: -193.7977
#> AIC: 393.5953
#> Convergence: FALSE
#>
#>
#> $tnorm
#> Model name: tnorm
#> omega mean sd
#> 150.45 60.97 26.72
#> Maximum LLF: -173.955
#> AIC: 353.91
#> Convergence: TRUE
#>
#>
#> $lnorm
#> Model name: lnorm
#> omega meanlog sdlog
#> 6188.497 8.270 1.839
#> Maximum LLF: -184.3571
#> AIC: 374.7142
#> Convergence: FALSE
#>
#>
#> $tlogis
#> Model name: tlogis
#> omega location scale
#> 153.37 62.34 16.17
#> Maximum LLF: -172.6565
#> AIC: 351.313
#> Convergence: TRUE
#>
#>
#> $llogis
#> Model name: llogis
#> omega locationlog scalelog
#> 392.6774 4.9056 0.5364
#> Maximum LLF: -181.6148
#> AIC: 369.2296
#> Convergence: TRUE
#>
#>
#> $txvmax
#> Model name: txvmax
#> omega loc scale
#> 208.49 62.51 38.96
#> Maximum LLF: -177.5718
#> AIC: 361.1435
#> Convergence: TRUE
#>
#>
#> $lxvmax
#> Model name: lxvmax
#> omega loclog scalelog
#> 18401.836 11.147 4.134
#> Maximum LLF: -186.8055
#> AIC: 379.611
#> Convergence: FALSE
#>
#>
#> $txvmin
#> Model name: txvmin
#> omega loc scale
#> 136.81 -66.09 18.22
#> Maximum LLF: -166.5841
#> AIC: 339.1683
#> Convergence: TRUE
#>
#>
#> $lxvmin
#> Model name: lxvmin
#> omega loclog scalelog
#> 183.4354 -4.4188 0.4831
#> Maximum LLF: -180.7614
#> AIC: 367.5227
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=sys1g, srms=result)
okamumu/Rsrat documentation built on Feb. 10, 2024, 11:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Rsrat
Rsrat provides the package to evalute the software reliability from the fault data collected in the testing phase. Rsrat can use two types of data; fault-detection time data and its grouped data. The fault-detection time data is a sequence of time intervals of fault detection times (CPU time, etc). Also its grouped data is a sequence of the number of detected faults for each time interval (per a working day, per a week, etc). The reliability evaluation is based on the software reliability growth model with NHPP (non-homogeneous Poisson process).
Installation
You can install Rsrat from GitHub with:
install.packages("devtools")
devtools::install_github("SwReliab/Rsrat")
Alternatively, you can use Remote to install Rsrat from GitHub
install.packages("remotes")
remotes::install_github("SwReliab/Rsrat")
Example
This is an example of the estimation of software reliability growth models from a fault data (tohma).
### load library
library(Rsrat)
### load example data
data(dacs)
### tohma is a grouped data
tohma
#> [1] 5 5 5 5 6 8 2 7 4 2 31 4 24 49 14 12 8 9 4 7 6 9 4 4 2
#> [26] 4 3 9 2 5 4 1 4 3 6 13 19 15 7 15 21 8 6 20 10 3 3 8 5 1
#> [51] 2 2 2 7 2 0 2 3 2 7 3 0 1 0 1 0 0 1 1 0 0 1 1 0 0
#> [76] 0 1 2 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 1
#> [101] 0 0 1 0 0 1 0 0 1 0 1
### Esimate all models and select the best one in terms of AIC
(result <- fit.srm.nhpp(fault=tohma))
#> Model name: lxvmin
#> omega loclog scalelog
#> 481.7029 -3.4642 0.6637
#> Maximum LLF: -316.2599
#> AIC: 638.5198
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=tohma, srms=result)
rateplot(fault=tohma, srms=result)
The second example illustrates the estimation for two specified models.
### All models in the package
srm.models
#> [1] "exp" "gamma" "pareto" "tnorm" "lnorm" "tlogis" "llogis" "txvmax"
#> [9] "lxvmax" "txvmin" "lxvmin"
### Estimate two models and no select
(result <- fit.srm.nhpp(fault=tohma, srm.names=c("exp", "gamma"), selection=NULL))
#> $exp
#> Model name: exp
#> omega rate
#> 497.2912 0.0308
#> Maximum LLF: -359.8777
#> AIC: 723.7555
#> Convergence: TRUE
#>
#>
#> $gamma
#> Model name: gamma
#> omega shape rate
#> 483.52301 1.88475 0.06447
#> Maximum LLF: -319.5695
#> AIC: 645.139
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=tohma, srms=result)
### Draw the graph (dmvf)
dmvfplot(fault=tohma, srms=result)
rateplot(fault=tohma, srms=result)
The third example shows the case where the fault data are fault detection data.
### fault-detection time data
#### Time intervals for all faults
#### The last value is a negative value, that indicates the time interval in which there is no fault detection after the last fault detection.
sys1
#> [1] 3 30 113 81 115 9 2 91 112 15 138 50
#> [13] 77 24 108 88 670 120 26 114 325 55 242 68
#> [25] 422 180 10 1146 600 15 36 4 0 8 227 65
#> [37] 176 58 457 300 97 263 452 255 197 193 6 79
#> [49] 816 1351 148 21 233 134 357 193 236 31 369 748
#> [61] 0 232 330 365 1222 543 10 16 529 379 44 129
#> [73] 810 290 300 529 281 160 828 1011 445 296 1755 1064
#> [85] 1783 860 983 707 33 868 724 2323 2930 1461 843 12
#> [97] 261 1800 865 1435 30 143 108 0 3110 1247 943 700
#> [109] 875 245 729 1897 447 386 446 122 990 948 1082 22
#> [121] 75 482 5509 100 10 1071 371 790 6150 3321 1045 648
#> [133] 5485 1160 1864 4116 -2526
### Esimate
(result <- fit.srm.nhpp(time=sys1[sys1>=0], te=-sys1[sys1<0]))
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Model name: gamma
#> omega shape rate
#> 1.545e+02 6.357e-01 1.618e-05
#> Maximum LLF: -967.1074
#> AIC: 1940.215
#> Convergence: TRUE
### Draw the graph
mvfplot(time=sys1[sys1>=0], te=-sys1[sys1<0], srms=result)
The fourth example illustrates the case where the mvfs for all the models are drawn.
### Esimate and return all the estimated results
(result <- fit.srm.nhpp(fault=sys1g, selection=NULL))
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> Warning in emfit(srm, data, initialize = TRUE, maxiter = con$maxiter, reltol =
#> con$reltol, : Did not converge to MLE by max iteration.
#> $exp
#> Model name: exp
#> omega rate
#> 4.295e+03 3.347e-04
#> Maximum LLF: -192.5611
#> AIC: 389.1221
#> Convergence: FALSE
#>
#>
#> $gamma
#> Model name: gamma
#> omega shape rate
#> 5.400e+02 1.799e+00 8.543e-03
#> Maximum LLF: -182.2326
#> AIC: 370.4651
#> Convergence: TRUE
#>
#>
#> $pareto
#> Model name: pareto
#> omega shape scale
#> 1179.22 56.39 44141.81
#> Maximum LLF: -193.7977
#> AIC: 393.5953
#> Convergence: FALSE
#>
#>
#> $tnorm
#> Model name: tnorm
#> omega mean sd
#> 150.45 60.97 26.72
#> Maximum LLF: -173.955
#> AIC: 353.91
#> Convergence: TRUE
#>
#>
#> $lnorm
#> Model name: lnorm
#> omega meanlog sdlog
#> 6188.497 8.270 1.839
#> Maximum LLF: -184.3571
#> AIC: 374.7142
#> Convergence: FALSE
#>
#>
#> $tlogis
#> Model name: tlogis
#> omega location scale
#> 153.37 62.34 16.17
#> Maximum LLF: -172.6565
#> AIC: 351.313
#> Convergence: TRUE
#>
#>
#> $llogis
#> Model name: llogis
#> omega locationlog scalelog
#> 392.6774 4.9056 0.5364
#> Maximum LLF: -181.6148
#> AIC: 369.2296
#> Convergence: TRUE
#>
#>
#> $txvmax
#> Model name: txvmax
#> omega loc scale
#> 208.49 62.51 38.96
#> Maximum LLF: -177.5718
#> AIC: 361.1435
#> Convergence: TRUE
#>
#>
#> $lxvmax
#> Model name: lxvmax
#> omega loclog scalelog
#> 18401.836 11.147 4.134
#> Maximum LLF: -186.8055
#> AIC: 379.611
#> Convergence: FALSE
#>
#>
#> $txvmin
#> Model name: txvmin
#> omega loc scale
#> 136.81 -66.09 18.22
#> Maximum LLF: -166.5841
#> AIC: 339.1683
#> Convergence: TRUE
#>
#>
#> $lxvmin
#> Model name: lxvmin
#> omega loclog scalelog
#> 183.4354 -4.4188 0.4831
#> Maximum LLF: -180.7614
#> AIC: 367.5227
#> Convergence: TRUE
### Draw the graph
mvfplot(fault=sys1g, srms=result)
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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