Minor fixes to the graph generator scripts in R, increased text size
Change-Id: Iaff237d0dcbd3518ca4ddb858d5bb0d07c3d6a05
diff --git a/TestON/JenkinsFile/scripts/SCPFIntentInstallWithdrawRerouteLat.R b/TestON/JenkinsFile/scripts/SCPFIntentInstallWithdrawRerouteLat.R
new file mode 100644
index 0000000..20b4ddd
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFIntentInstallWithdrawRerouteLat.R
@@ -0,0 +1,200 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 9 ] ) ){
+ print( "Usage: Rscript SCPFIntentInstallWithdrawRerouteLat.R <isFlowObj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <batch-size> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+flowObjFileModifier <- ""
+if ( args[ 1 ] == "y" ){
+ flowObjFileModifier <- "fobj_"
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 9 ], "SCPFIntentInstallWithdrawRerouteLat", sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 7 ], sep="_" )
+if ( args[ 1 ] == "y" ){
+ errBarOutputFile <- paste( errBarOutputFile, "_fobj", sep="" )
+}
+errBarOutputFile <- paste( errBarOutputFile, "_", sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 8 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, "-batchSize", sep="" )
+errBarOutputFile <- paste( errBarOutputFile, "_graph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 2 ], port=strtoi( args[ 3 ] ), user=args[ 4 ],password=args[ 5 ] )
+
+command1 <- paste( "SELECT * FROM intent_latency_", flowObjFileModifier, sep="" )
+command1 <- paste( command1, "tests WHERE batch_size=", sep="" )
+command1 <- paste( command1, args[ 8 ], sep="" )
+command1 <- paste( command1, " AND branch = '", sep="" )
+command1 <- paste( command1, args[ 7 ], sep="" )
+command1 <- paste( command1, "' AND date IN ( SELECT MAX( date ) FROM intent_latency_", sep="" )
+command1 <- paste( command1, flowObjFileModifier, sep="" )
+command1 <- paste( command1, "tests WHERE branch='", sep="" )
+command1 <- paste( command1, args[ 7 ], sep="" )
+command1 <- paste( command1, "')", sep="" )
+
+print( paste( "Sending SQL command:", command1 ) )
+
+fileData1 <- dbGetQuery( con, command1 )
+
+command2 <- paste( "SELECT * FROM intent_reroute_latency_", flowObjFileModifier, sep="" )
+command2 <- paste( command2, "tests WHERE batch_size=", sep="" )
+command2 <- paste( command2, args[ 8 ], sep="" )
+command2 <- paste( command2, " AND branch = '", sep="" )
+command2 <- paste( command2, args[ 7 ], sep="" )
+command2 <- paste( command2, "' AND date IN ( SELECT MAX( date ) FROM intent_reroute_latency_", sep="" )
+command2 <- paste( command2, flowObjFileModifier, sep="" )
+command2 <- paste( command2, "tests WHERE branch='", sep="" )
+command2 <- paste( command2, args[ 7 ], sep="" )
+command2 <- paste( command2, "')", sep="" )
+
+print( paste( "Sending SQL command:", command2 ) )
+
+fileData2 <- dbGetQuery( con, command2 )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data." )
+if ( ncol( fileData2 ) == 0 ){
+ avgs <- c( fileData1[ 'install_avg' ], fileData1[ 'withdraw_avg' ] )
+} else{
+ colnames( fileData2 ) <- c( "date", "name", "date", "branch", "commit", "scale", "batch_size", "reroute_avg", "reroute_std" )
+ avgs <- c( fileData1[ 'install_avg' ], fileData1[ 'withdraw_avg' ], fileData2[ 'reroute_avg' ] )
+}
+
+# Parse lists into data frames.
+dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
+
+if ( ncol( fileData2 ) == 0 ){
+ dataFrame$scale <- c( fileData1$scale, fileData1$scale ) # Add node scaling to the data frame.
+ dataFrame$stds <- c( fileData1$install_std, fileData1$withdraw_std )
+} else{
+ dataFrame$scale <- c( fileData1$scale, fileData1$scale, fileData2$scale ) # Add node scaling to the data frame.
+ dataFrame$stds <- c( fileData1$install_std, fileData1$withdraw_std, fileData2$reroute_std )
+}
+colnames( dataFrame ) <- c( "ms", "type", "scale", "stds" )
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$type <- as.character( dataFrame$type )
+dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+# 1. Graph fundamental data is generated first.
+# These are variables that apply to all of the graphs being generated, regardless of type.
+#
+# 2. Type specific graph data is generated.
+# Data specific for the error bar and stacked bar graphs are generated.
+#
+# 3. Generate and save the graphs.
+# Graphs are saved to the filename above, in the directory provided in command line args
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+# Calculate window to display graph, based on the lowest and highest points of the data.
+if ( min( dataFrame$ms - dataFrame$stds ) < 0){
+ yWindowMin <- min( dataFrame$ms - dataFrame$stds ) * 1.05
+} else {
+ yWindowMin <- 0
+}
+yWindowMax <- max( dataFrame$ms + dataFrame$stds )
+
+mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = ms, ymin = ms - stds, ymax = ms + stds,fill = type ) )
+
+# Formatting the plot
+width <- 1.3 # Width of the bars.
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+yLimit <- ylim( yWindowMin, yWindowMax )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+chartTitle <- "Intent Install, Withdraw, & Reroute Latencies"
+if ( args[ 1 ] == "y" ){
+ chartTitle <- paste( chartTitle, "w/ FlowObj" )
+}
+chartTitle <- paste( chartTitle, "\nBatch Size =" )
+chartTitle <- paste( chartTitle, fileData1[ 1,'batch_size' ] )
+
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + xScaleConfig + yLimit + xLabel + yLabel + fillLabel + theme
+
+
+# Create the bar graph with error bars.
+# geom_bar contains:
+# - stat: data formatting (usually "identity")
+# - width: the width of the bar types (declared above)
+# geom_errorbar contains similar arguments as geom_bar.
+print( "Generating bar graph with error bars." )
+barGraphFormat <- geom_bar( stat = "identity", width = width, position = "dodge" )
+errorBarFormat <- geom_errorbar( width = width, position = "dodge" )
+title <- ggtitle( chartTitle )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+# Save graph to file
+print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote bar chart with error bars out to", errBarOutputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFLineGraph.R b/TestON/JenkinsFile/scripts/SCPFLineGraph.R
new file mode 100644
index 0000000..4b1ead6
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFLineGraph.R
@@ -0,0 +1,147 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# This is the R script that generates the SCPF front page graphs.
+
+# **********************************************************
+# STEP 1: Data management.
+# **********************************************************
+
+print( "STEP 1: Data management." )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+# RPostgreSQL: https://code.google.com/archive/p/rpostgresql/
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL )
+
+# Command line arguments are read. Args include the database credentials, test name, branch name, and the directory to output files.
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 10 ] ) ){
+ print( "Usage: Rscript testresultgraph.R <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <#-dates> <SQL-command> <y-axis> <directory-to-save-graph>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for the output graph include the testname, branch, and the graph type.
+
+outputFile <- paste( args[ 10 ], "SCPF_Front_Page" , sep="" )
+outputFile <- paste( outputFile, gsub( " ", "_", args[ 5 ] ), sep="_" )
+outputFile <- paste( outputFile, args[ 6 ], sep="_" )
+outputFile <- paste( outputFile, args[ 7 ], sep="_" )
+outputFile <- paste( outputFile, "dates", sep="-" )
+outputFile <- paste( outputFile, "_graph.jpg", sep="" )
+
+# From RPostgreSQL
+print( "Reading from databases." )
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+print( "Sending SQL command." )
+fileData <- dbGetQuery( con, args[ 8 ] )
+
+# Title of graph based on command line args.
+title <- args[ 5 ]
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data into new data frame." )
+
+if ( ncol( fileData ) > 1 ){
+ for ( i in 2:ncol( fileData ) ){
+ fileData[ i ] <- fileData[ i - 1 ] + fileData[ i ]
+ }
+}
+
+# Parse lists into data frames.
+# This is where reshape2 comes in. Avgs list is converted to data frame.
+dataFrame <- melt( fileData )
+
+dataFrame$date <- fileData$date
+
+colnames( dataFrame ) <- c( "Legend", "Values" )
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$Legend <- as.character( dataFrame$Legend )
+dataFrame$Legend <- factor( dataFrame$Legend, levels=unique( dataFrame$Legend ) )
+
+# Adding a temporary iterative list to the dataFrame so that there are no gaps in-between date numbers.
+dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+print( "Creating main plot." )
+# Create the primary plot here.
+# ggplot contains the following arguments:
+# - data: the data frame that the graph will be based off of
+# - aes: the asthetics of the graph which require:
+# - x: x-axis values (usually iterative, but it will become date # later)
+# - y: y-axis values (usually tests)
+# - color: the category of the colored lines (usually legend of test)
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = Values, color = Legend ) )
+
+print( "Formatting main plot." )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + expand_limits( y = 0 )
+
+yScaleConfig <- scale_y_continuous( breaks = seq( 0, max( dataFrame$Values ) * 1.05, by = ceiling( max( dataFrame$Values ) / 10 ) ) )
+
+xLabel <- xlab( "Date" )
+yLabel <- ylab( args[ 9 ] )
+fillLabel <- labs( fill="Type" )
+legendLabels <- scale_colour_discrete( labels = names( fileData ) )
+centerTitle <- theme( plot.title=element_text( hjust = 0.5 ) ) # To center the title text
+theme <- theme( axis.text.x = element_blank(), axis.ticks.x = element_blank(), plot.title = element_text( size = 28, face='bold' ) )
+
+fundamentalGraphData <- fundamentalGraphData + yScaleConfig + xLabel + yLabel + fillLabel + legendLabels + centerTitle + theme
+print( "Generating line graph." )
+
+lineGraphFormat <- geom_line()
+pointFormat <- geom_point( size = 0.2 )
+title <- ggtitle( title )
+
+result <- fundamentalGraphData + lineGraphFormat + pointFormat + title
+
+# Save graph to file
+print( paste( "Saving result graph to", outputFile ) )
+ggsave( outputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote result graph out to", outputFile ) )
\ No newline at end of file
diff --git a/TestON/JenkinsFile/scripts/SCPFbatchFlowResp.R b/TestON/JenkinsFile/scripts/SCPFbatchFlowResp.R
new file mode 100644
index 0000000..476cafb
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFbatchFlowResp.R
@@ -0,0 +1,182 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFbatchFlowResp <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
+errBarOutputFile <- paste( errBarOutputFile, "_PostGraph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM batch_flow_tests WHERE branch='", args[ 6 ], sep="" )
+command <- paste( command, "' ORDER BY date DESC LIMIT 3", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+chartTitle <- paste( "Single Bench Flow Latency - Post", "Last 3 Builds", sep = "\n" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+avgs <- c()
+
+print( "Sorting data." )
+avgs <- c( fileData[ 'posttoconfrm' ], fileData[ 'elapsepost' ] )
+
+dataFrame <- melt( avgs )
+dataFrame$scale <- fileData$scale
+dataFrame$date <- fileData$date
+dataFrame$iterative <- dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
+
+colnames( dataFrame ) <- c( "ms", "type", "scale", "date", "iterative" )
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$type <- as.character( dataFrame$type )
+dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, fill = type ) )
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$date )
+xLabel <- xlab( "date" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph with error bars." )
+width <- 0.3
+barGraphFormat <- geom_bar( stat="identity", width = width )
+title <- ggtitle( chartTitle )
+result <- fundamentalGraphData + barGraphFormat + title
+
+
+print( paste( "Saving bar chart to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+print( paste( "Successfully wrote stacked bar chart out to", errBarOutputFile ) )
+
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+avgs <- c()
+
+print( "Sorting data." )
+avgs <- c( fileData[ 'deltoconfrm' ], fileData[ 'elapsedel' ] )
+
+dataFrame <- melt( avgs )
+dataFrame$scale <- fileData$scale
+dataFrame$date <- fileData$date
+dataFrame$iterative <- dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
+
+colnames( dataFrame ) <- c( "ms", "type", "scale", "date", "iterative" )
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$type <- as.character( dataFrame$type )
+dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, fill = type ) )
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$date )
+xLabel <- xlab( "Build Date" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph with error bars." )
+width <- 0.3
+barGraphFormat <- geom_bar( stat="identity", width = width )
+chartTitle <- paste( "Single Bench Flow Latency - Del", "Last 3 Builds", sep = "\n" )
+title <- ggtitle( chartTitle )
+result <- fundamentalGraphData + barGraphFormat + title
+
+errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
+errBarOutputFile <- paste( errBarOutputFile, "_DelGraph.jpg", sep="" )
+
+print( paste( "Saving bar chart to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+print( paste( "Successfully wrote stacked bar chart out to", errBarOutputFile ) )
\ No newline at end of file
diff --git a/TestON/JenkinsFile/scripts/SCPFcbench.R b/TestON/JenkinsFile/scripts/SCPFcbench.R
new file mode 100644
index 0000000..786a2fa
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFcbench.R
@@ -0,0 +1,122 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Normal usage
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFcbench <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
+errBarOutputFile <- paste( errBarOutputFile, "_errGraph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM cbench_bm_tests WHERE branch='", args[ 6 ], sep="" )
+command <- paste( command, "' ORDER BY date DESC LIMIT 3", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+chartTitle <- paste( "Single-Node CBench Throughput", "Last 3 Builds", sep = "\n" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+fileDataNames <- names( fileData )
+
+avgs <- c()
+stds <- c()
+
+print( "Sorting data." )
+avgs <- c( fileData[ 'avg' ] )
+
+dataFrame <- melt( avgs )
+dataFrame$std <- c( fileData$std )
+dataFrame$date <- c( fileData$date )
+dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
+
+colnames( dataFrame ) <- c( "ms", "type", "std", "date", "iterative" )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, ymin = ms - std, ymax = ms + std ) )
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$date )
+xLabel <- xlab( "date" )
+yLabel <- ylab( "Responses / sec" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph with error bars." )
+width <- 0.3
+barGraphFormat <- geom_bar( stat="identity", position = position_dodge(), width = width, fill="#00AA13" )
+errorBarFormat <- geom_errorbar( position=position_dodge( ), width = width )
+title <- ggtitle( chartTitle )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote bar chart with error bars out to", errBarOutputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFflowTp1g.R b/TestON/JenkinsFile/scripts/SCPFflowTp1g.R
new file mode 100644
index 0000000..2116035
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFflowTp1g.R
@@ -0,0 +1,183 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Normal usage
+# Check if sufficient args are provided.
+if ( is.na( args[ 9 ] ) ){
+ print( "Usage: Rscript SCPFflowTp1g.R <has-flow-obj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <has-neighbors> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 9 ], args[ 6 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 7 ], sep="_" )
+if ( args[ 8 ] == 'y' ){
+ errBarOutputFile <- paste( errBarOutputFile, "all-neighbors", sep="_" )
+} else {
+ errBarOutputFile <- paste( errBarOutputFile, "no-neighbors", sep="_" )
+}
+if ( args[ 1 ] == 'y' ){
+ errBarOutputFile <- paste( errBarOutputFile, "flowObj", sep="_")
+}
+errBarOutputFile <- paste( errBarOutputFile, "_graph.jpg", sep="" )
+
+print( "Reading from databases." )
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 2 ], port=strtoi( args[ 3 ] ), user=args[ 4 ],password=args[ 5 ] )
+
+commandNeighborModifier <- ""
+flowObjModifier <- ""
+if ( args[ 1 ] == 'y' ){
+ flowObjModifier <- "_fobj"
+}
+if ( args[ 8 ] == 'y' ){
+ commandNeighborModifier <- "scale=1 OR NOT "
+}
+
+command <- paste( "SELECT scale, avg( avg ), avg( std ) FROM flow_tp", flowObjModifier, sep="" )
+command <- paste( command, "_tests WHERE (", sep="" )
+command <- paste( command, commandNeighborModifier, sep="" )
+command <- paste( command, "neighbors = 0 ) AND branch = '", sep="" )
+command <- paste( command, args[ 7 ], sep="" )
+command <- paste( command, "' AND date IN ( SELECT max( date ) FROM flow_tp", sep="" )
+command <- paste( command, flowObjModifier, sep="" )
+command <- paste( command, "_tests WHERE branch='", sep="" )
+command <- paste( command, args[ 7 ], sep="" )
+command <- paste( command, "' ) GROUP BY scale ORDER BY scale", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+title <- paste( args[ 6 ], args[ 7 ], sep="_" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data." )
+colnames( fileData ) <- c( "scale", "avg", "std" )
+avgs <- c( fileData[ 'avg' ] )
+
+# Parse lists into data frames.
+dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
+dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
+dataFrame$std <- fileData$std
+
+colnames( dataFrame ) <- c( "throughput", "type", "scale", "std" )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+# 1. Graph fundamental data is generated first.
+# These are variables that apply to all of the graphs being generated, regardless of type.
+#
+# 2. Type specific graph data is generated.
+# Data specific for the error bar and stacked bar graphs are generated.
+#
+# 3. Generate and save the graphs.
+# Graphs are saved to the filename above, in the directory provided in command line args
+
+print( "Generating fundamental graph data." )
+
+# Create the primary plot here.
+# ggplot contains the following arguments:
+# - data: the data frame that the graph will be based off of
+# - aes: the asthetics of the graph which require:
+# - x: x-axis values (usually node scaling)
+# - y: y-axis values (usually time in milliseconds)
+# - fill: the category of the colored side-by-side bars (usually type)
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = throughput, ymin = throughput - std, ymax = throughput + std, fill = type ) )
+
+# Formatting the plot
+width <- 0.7 # Width of the bars.
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$scale, label = dataFrame$scale )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Throughput (events/second)" )
+fillLabel <- labs( fill="Type" )
+chartTitle <- "Flow Throughput Test"
+if ( args[ 1 ] == 'y' ){
+ chartTitle <- paste( chartTitle, " with Flow Objectives", sep="" )
+}
+chartTitle <- paste( chartTitle, "\nNeighbors =", sep="" )
+if ( args[ 8 ] == 'y' ){
+ chartTitle <- paste( chartTitle, "Cluster Size - 1" )
+} else {
+ chartTitle <- paste( chartTitle, "0" )
+}
+
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+# Create the stacked bar graph with error bars.
+# geom_bar contains:
+# - stat: data formatting (usually "identity")
+# - width: the width of the bar types (declared above)
+# geom_errorbar contains similar arguments as geom_bar.
+print( "Generating bar graph with error bars." )
+barGraphFormat <- geom_bar( stat = "identity", width = width, fill="#FFA94F" )
+errorBarFormat <- geom_errorbar( position=position_dodge( ), width = width )
+title <- ggtitle( paste( chartTitle, "" ) )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+# Save graph to file
+print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote bar chart with error bars out to", errBarOutputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFhostLat.R b/TestON/JenkinsFile/scripts/SCPFhostLat.R
new file mode 100644
index 0000000..75e2a45
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFhostLat.R
@@ -0,0 +1,123 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFhostLat <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
+errBarOutputFile <- paste( errBarOutputFile, "_errGraph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM host_latency_tests WHERE branch = '", args[ 6 ], sep = "" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM host_latency_tests WHERE branch = '", sep = "" )
+command <- paste( command, args[ 6 ], sep = "" )
+command <- paste( command, "' ) ", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+chartTitle <- "Host Latency"
+
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+avgs <- c()
+
+print( "Sorting data." )
+avgs <- c( fileData[ 'avg' ] )
+
+dataFrame <- melt( avgs )
+dataFrame$scale <- fileData$scale
+dataFrame$std <- fileData$std
+
+colnames( dataFrame ) <- c( "ms", "type", "scale", "std" )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = ms, ymin = ms - std, ymax = ms + std ) )
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph with error bars." )
+width <- 0.9
+barGraphFormat <- geom_bar( stat="identity", position=position_dodge( ), width = width, fill="#E8BD00" )
+errorBarFormat <- geom_errorbar( position=position_dodge( ), width = width )
+title <- ggtitle( paste( chartTitle, "with Standard Error Bars" ) )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+print( paste( "Successfully wrote bar chart out to", errBarOutputFile ) )
\ No newline at end of file
diff --git a/TestON/JenkinsFile/scripts/SCPFintentEventTp.R b/TestON/JenkinsFile/scripts/SCPFintentEventTp.R
new file mode 100644
index 0000000..a8d8248
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFintentEventTp.R
@@ -0,0 +1,181 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Normal usage
+# Check if sufficient args are provided.
+if ( is.na( args[ 9 ] ) ){
+ print( "Usage: Rscript SCPFIntentEventTp.R <has-flow-obj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <has-neighbors> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 9 ], args[ 6 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 7 ], sep="_" )
+if ( args[ 8 ] == 'y' ){
+ errBarOutputFile <- paste( errBarOutputFile, "all-neighbors", sep="_" )
+} else {
+ errBarOutputFile <- paste( errBarOutputFile, "no-neighbors", sep="_" )
+}
+if ( args[ 1 ] == 'y' ){
+ errBarOutputFile <- paste( errBarOutputFile, "flowObj", sep="_")
+}
+errBarOutputFile <- paste( errBarOutputFile, "_graph.jpg", sep="" )
+
+print( "Reading from databases." )
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 2 ], port=strtoi( args[ 3 ] ), user=args[ 4 ],password=args[ 5 ] )
+
+commandNeighborModifier <- ""
+flowObjModifier <- ""
+if ( args[ 1 ] == 'y' ){
+ flowObjModifier <- "_fobj"
+}
+if ( args[ 8 ] == 'y' ){
+ commandNeighborModifier <- "scale=1 OR NOT "
+}
+
+command <- paste( "SELECT scale, SUM( avg ) as avg FROM intent_tp", flowObjModifier, sep="" )
+command <- paste( command, "_tests WHERE (", sep="" )
+command <- paste( command, commandNeighborModifier, sep="" )
+command <- paste( command, "neighbors = 0 ) AND branch = '", sep="")
+command <- paste( command, args[ 7 ], sep="" )
+command <- paste( command, "' AND date IN ( SELECT max( date ) FROM intent_tp", sep="" )
+command <- paste( command, flowObjModifier, sep="" )
+command <- paste( command, "_tests WHERE branch='", sep="" )
+command <- paste( command, args[ 7 ], sep="" )
+command <- paste( command, "' ) GROUP BY scale ORDER BY scale", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+title <- paste( args[ 6 ], args[ 7 ], sep="_" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data." )
+avgs <- c( fileData[ 'avg' ] )
+
+# Parse lists into data frames.
+dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
+dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
+
+colnames( dataFrame ) <- c( "throughput", "type", "scale" )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+# 1. Graph fundamental data is generated first.
+# These are variables that apply to all of the graphs being generated, regardless of type.
+#
+# 2. Type specific graph data is generated.
+# Data specific for the error bar and stacked bar graphs are generated.
+#
+# 3. Generate and save the graphs.
+# Graphs are saved to the filename above, in the directory provided in command line args
+
+print( "Generating fundamental graph data." )
+
+# Create the primary plot here.
+# ggplot contains the following arguments:
+# - data: the data frame that the graph will be based off of
+# - aes: the asthetics of the graph which require:
+# - x: x-axis values (usually node scaling)
+# - y: y-axis values (usually time in milliseconds)
+# - fill: the category of the colored side-by-side bars (usually type)
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = throughput, fill = type ) )
+
+# Formatting the plot
+width <- 0.7 # Width of the bars.
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$scale, label = dataFrame$scale )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Throughput (events/second)" )
+fillLabel <- labs( fill="Type" )
+chartTitle <- "Intent Event Throughput"
+if ( args[ 1 ] == 'y' ){
+ chartTitle <- paste( chartTitle, " With Flow Objectives", sep="" )
+}
+chartTitle <- paste( chartTitle, "\nevents/second with Neighbors =", sep="" )
+if ( args[ 8 ] == 'y' ){
+ chartTitle <- paste( chartTitle, "all" )
+} else {
+ chartTitle <- paste( chartTitle, "0" )
+}
+
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+# Create the stacked bar graph with error bars.
+# geom_bar contains:
+# - stat: data formatting (usually "identity")
+# - width: the width of the bar types (declared above)
+# geom_errorbar contains similar arguments as geom_bar.
+print( "Generating bar graph." )
+barGraphFormat <- geom_bar( stat = "identity", width = width, fill="#169EFF" )
+title <- ggtitle( paste( chartTitle, "" ) )
+result <- fundamentalGraphData + barGraphFormat + title
+
+# Save graph to file
+print( paste( "Saving bar chart to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+print( paste( "Successfully wrote bar chart out to", errBarOutputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFmastershipFailoverLat.R b/TestON/JenkinsFile/scripts/SCPFmastershipFailoverLat.R
new file mode 100644
index 0000000..594a515
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFmastershipFailoverLat.R
@@ -0,0 +1,157 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Normal usage
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFmastershipFailoverLat <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+errBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+errBarOutputFile <- paste( errBarOutputFile, args[ 6 ], sep="_" )
+errBarOutputFile <- paste( errBarOutputFile, "_errGraph.jpg", sep="" )
+
+stackedBarOutputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+stackedBarOutputFile <- paste( stackedBarOutputFile, args[ 6 ], sep="_" )
+stackedBarOutputFile <- paste( stackedBarOutputFile, "_stackedGraph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM mastership_failover_tests WHERE branch = '", args[ 6 ], sep = "" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM mastership_failover_tests WHERE branch = '", sep = "" )
+command <- paste( command, args[ 6 ], sep = "" )
+command <- paste( command, "' ) ", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+chartTitle <- "Mastership Failover Latency"
+
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+fileDataNames <- names( fileData )
+
+avgs <- c()
+stds <- c()
+
+
+print( "Sorting data." )
+for ( name in fileDataNames ){
+ nameLen <- nchar( name )
+ if ( nameLen > 2 ){
+ if ( substring( name, nameLen - 2, nameLen ) == "avg" ){
+ avgs <- c( avgs, fileData[ name ] )
+ }
+ if ( substring( name, nameLen - 2, nameLen ) == "std" ){
+ stds <- c( stds, fileData[ name ] )
+ }
+ }
+}
+
+avgData <- melt( avgs )
+avgData$scale <- fileData$scale
+colnames( avgData ) <- c( "ms", "type", "scale" )
+
+stdData <- melt( stds )
+colnames( stdData ) <- c( "ms", "type" )
+
+dataFrame <- na.omit( avgData ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( avgData )
+
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = avgData, aes( x = scale, y = ms, ymin = ms - stdData$ms, ymax = ms + stdData$ms,fill = type ) )
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+yLimit <- ylim( yMin, yMax )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + yLimit + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph with error bars." )
+width <- 0.9
+barGraphFormat <- geom_bar( stat="identity", position=position_dodge( ), width = width )
+errorBarFormat <- geom_errorbar( position=position_dodge( ), width = width )
+title <- ggtitle( paste( chartTitle, "with Standard Error Bars" ) )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars to", errBarOutputFile ) )
+ggsave( errBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote bar chart with error bars out to", errBarOutputFile ) )
+
+
+print( "Generating stacked bar chart." )
+stackedBarFormat <- geom_bar( stat="identity", width=width )
+title <- ggtitle( paste( chartTitle, "Total Latency" ) )
+result <- fundamentalGraphData + stackedBarFormat + title
+
+
+print( paste( "Saving stacked bar chart to", stackedBarOutputFile ) )
+ggsave( stackedBarOutputFile, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote stacked bar chart out to", stackedBarOutputFile ) )
\ No newline at end of file
diff --git a/TestON/JenkinsFile/scripts/SCPFportLat.R b/TestON/JenkinsFile/scripts/SCPFportLat.R
new file mode 100644
index 0000000..9eb38a5
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFportLat.R
@@ -0,0 +1,177 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFportLat <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+errBarOutputFileUp <- paste( args[ 7 ], "SCPFportLat_", sep = "" )
+errBarOutputFileUp <- paste( errBarOutputFileUp, args[ 6 ], sep = "" )
+errBarOutputFileUp <- paste( errBarOutputFileUp, "_UpErrBarWithStack.jpg", sep = "" )
+
+errBarOutputFileDown <- paste( args[ 7 ], "SCPFportLat_", sep = "" )
+errBarOutputFileDown <- paste( errBarOutputFileDown, args[ 6 ], sep = "" )
+errBarOutputFileDown <- paste( errBarOutputFileDown, "_DownErrBarWithStack.jpg", sep = "" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM port_latency_details WHERE branch = '", args[ 6 ], sep = "" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM port_latency_details WHERE branch = '", sep = "" )
+command <- paste( command, args[ 6 ], sep = "" )
+command <- paste( command, "' ) ", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+chartTitle <- paste( "Port Latency", args[ 6 ], sep = " - " )
+chartTitle <- paste( chartTitle, "\n" )
+
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "Sorting data." )
+
+upAvgs <- c( fileData[ 'up_ofp_to_dev_avg' ], fileData[ 'up_dev_to_link_avg' ], fileData[ 'up_link_to_graph_avg' ] )
+upAvgsData <- melt( upAvgs )
+upAvgsData$scale <- fileData$scale
+upAvgsData$up_std <- fileData$up_std
+
+
+colnames( upAvgsData ) <- c( "ms", "type", "scale", "stds" )
+upAvgsData$type <- as.character( upAvgsData$type )
+upAvgsData$type <- factor( upAvgsData$type, levels=unique( upAvgsData$type ) )
+
+downAvgs <- c( fileData[ 'down_ofp_to_dev_avg' ], fileData[ 'down_dev_to_link_avg' ], fileData[ 'down_link_to_graph_avg' ] )
+downAvgsData <- melt( downAvgs )
+downAvgsData$scale <- fileData$scale
+downAvgsData$down_std <- fileData$down_std
+
+colnames( downAvgsData ) <- c( "ms", "type", "scale", "stds" )
+downAvgsData$type <- as.character( downAvgsData$type )
+downAvgsData$type <- factor( downAvgsData$type, levels=unique( downAvgsData$type ) )
+
+upAvgsData <- na.omit( upAvgsData ) # Omit any data that doesn't exist
+downAvgsData <- na.omit( downAvgsData ) # Omit any data that doesn't exist
+
+print( "Up Averages Results:" )
+print( upAvgsData )
+
+print( "Down Averages Results:" )
+print( downAvgsData )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+
+print( "Generating fundamental graph data (Port Up Latency)." )
+width <- 1
+ if ( min( fileData[ 'up_end_to_end_avg' ] - upAvgsData$stds ) < 0 ) {
+ yMin <- min( fileData[ 'up_end_to_end_avg' ] - upAvgsData$stds ) * 1.05
+ } else {
+ yMin <- 0
+ }
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+yMax <- max( fileData[ 'up_end_to_end_avg' ] + upAvgsData$stds )
+
+mainPlot <- ggplot( data = upAvgsData, aes( x = scale, y = ms, fill = type, ymin = fileData[ 'up_end_to_end_avg' ] - stds, ymax = fileData[ 'up_end_to_end_avg' ] + stds ) )
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+yLimit <- ylim( yMin, yMax )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + yLimit + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+print( "Generating bar graph with error bars (Port Up Latency)." )
+barGraphFormat <- geom_bar( stat="identity", width = width )
+errorBarFormat <- geom_errorbar( width = width )
+
+title <- ggtitle( "Port Up Latency" )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars (Port Up Latency) to", errBarOutputFileUp ) )
+ggsave( errBarOutputFileUp, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote bar chart with error bars (Port Up Latency) out to", errBarOutputFileUp ) )
+
+
+print( "Generating fundamental graph data (Port Down Latency)." )
+ if ( min( fileData[ 'down_end_to_end_avg' ] - downAvgsData$stds ) < 0 ) {
+ yMin <- min( fileData[ 'down_end_to_end_avg' ] - downAvgsData$stds )
+ } else {
+ yMin <- 0
+ }
+ yMax <- max( fileData[ 'down_end_to_end_avg' ] + downAvgsData$stds )
+
+mainPlot <- ggplot( data = downAvgsData, aes( x = scale, y = ms, fill = type, ymin = fileData[ 'down_end_to_end_avg' ] - stds, ymax = fileData[ 'down_end_to_end_avg' ] + stds ) )
+yLimit <- ylim( yMin, yMax )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + yLimit + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+print( "Generating bar graph with error bars (Port Down Latency)." )
+barGraphFormat <- geom_bar( stat="identity", width = width )
+errorBarFormat <- geom_errorbar( width = width )
+
+title <- ggtitle( "Port Down Latency" )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars (Port Down Latency) to", errBarOutputFileDown ) )
+ggsave( errBarOutputFileDown, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote bar chart with error bars (Port Down Latency) out to", errBarOutputFileDown ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFscaleTopo.R b/TestON/JenkinsFile/scripts/SCPFscaleTopo.R
new file mode 100644
index 0000000..87f1f57
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFscaleTopo.R
@@ -0,0 +1,162 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFgraphGenerator <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+outputFile <- paste( args[ 7 ], args[ 5 ], sep="" )
+outputFile <- paste( outputFile, args[ 6 ], sep="_" )
+outputFile <- paste( outputFile, "_graph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM scale_topo_latency_details WHERE branch = '", args[ 6 ], sep = "" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM scale_topo_latency_details WHERE branch = '", sep = "" )
+command <- paste( command, args[ 6 ], sep = "" )
+command <- paste( command, "' ) ", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+title <- paste( args[ 5 ], args[ 6 ], sep="_" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data." )
+avgs <- c( fileData[ 'last_role_request_to_last_topology' ], fileData[ 'last_connection_to_last_role_request' ], fileData[ 'first_connection_to_last_connection' ] )
+
+# Parse lists into data frames.
+dataFrame <- melt( avgs ) # This is where reshape2 comes in. Avgs list is converted to data frame
+dataFrame$scale <- fileData$scale # Add node scaling to the data frame.
+colnames( dataFrame ) <- c( "ms", "type", "scale")
+
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$type <- as.character( dataFrame$type )
+dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )
+dataFrame$iterative <- seq( 1, nrow( fileData ), by = 1 )
+
+# Obtain the sum of the averages for the plot size and center of standard deviation bars.
+avgsSum <- fileData$total_time
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+# 1. Graph fundamental data is generated first.
+# These are variables that apply to all of the graphs being generated, regardless of type.
+#
+# 2. Type specific graph data is generated.
+# Data specific for the error bar and stacked bar graphs are generated.
+#
+# 3. Generate and save the graphs.
+# Graphs are saved to the filename above, in the directory provided in command line args
+
+print( "Generating fundamental graph data." )
+
+# Calculate window to display graph, based on the lowest and highest points of the data.
+if ( min( avgsSum ) < 0){
+ yWindowMin <- min( avgsSum ) * 1.05
+} else {
+ yWindowMin <- 0
+}
+yWindowMax <- max( avgsSum )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+# Create the primary plot here.
+# ggplot contains the following arguments:
+# - data: the data frame that the graph will be based off of
+# - aes: the asthetics of the graph which require:
+# - x: x-axis values (usually node scaling)
+# - y: y-axis values (usually time in milliseconds)
+# - fill: the category of the colored side-by-side bars (usually type)
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = ms, fill = type ) )
+
+# Formatting the plot
+width <- 0.6 # Width of the bars.
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$scale )
+yLimit <- ylim( yWindowMin, yWindowMax )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+chartTitle <- paste( "Scale Topology Latency Test" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + xScaleConfig + yLimit + xLabel + yLabel + fillLabel + theme
+
+# Create the stacked bar graph with error bars.
+# geom_bar contains:
+# - stat: data formatting (usually "identity")
+# - width: the width of the bar types (declared above)
+# geom_errorbar contains similar arguments as geom_bar.
+print( "Generating bar graph with error bars." )
+barGraphFormat <- geom_bar( stat = "identity", width = width )
+title <- ggtitle( paste( chartTitle, "" ) )
+result <- fundamentalGraphData + barGraphFormat + title
+
+# Save graph to file
+print( paste( "Saving bar chart with error bars to", outputFile ) )
+ggsave( outputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote bar chart with error bars out to", outputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFscalingMaxIntents.R b/TestON/JenkinsFile/scripts/SCPFscalingMaxIntents.R
new file mode 100644
index 0000000..e5f54e5
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFscalingMaxIntents.R
@@ -0,0 +1,140 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Normal usage
+# Check if sufficient args are provided.
+if ( is.na( args[ 8 ] ) ){
+ print( "Usage: Rscript SCPFInstalledIntentsFlows <has-flowObj> <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+
+outputFile <- paste( args[ 8 ], args[ 6 ], sep="" )
+if ( args[ 1 ] == "y" ){
+ outputFile <- paste( outputFile, "flowObj", sep="_" )
+}
+outputFile <- paste( outputFile, args[ 7 ], sep="_" )
+outputFile <- paste( outputFile, "_errGraph.jpg", sep="" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 2 ], port=strtoi( args[ 3 ] ), user=args[ 4 ],password=args[ 5 ] )
+
+command <- "SELECT * FROM max_intents_"
+if ( args[ 1 ] == "y" ){
+ command <- paste( command, "fobj_", sep="" )
+}
+command <- paste( command, "tests WHERE branch = '", sep = "" )
+command <- paste( command, args[ 7 ], sep="" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM max_intents_", sep="" )
+if ( args[ 1 ] == "y" ){
+ command <- paste( command, "fobj_", sep="" )
+}
+command <- paste( command, "tests WHERE branch = '", sep = "" )
+command <- paste( command, args[ 7 ], sep = "" )
+command <- paste( command, "' ) ", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+if ( args[ 1 ] == "y" ){
+ chartTitle <- "Number of Installed Intents & Flows with Flow Objectives"
+} else {
+ chartTitle <- "Number of Installed Intents & Flows"
+}
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+fileDataNames <- names( fileData )
+
+avgs <- c()
+
+print( "Sorting data." )
+avgs <- c( fileData[ 'max_intents_ovs' ], fileData[ 'max_flows_ovs' ] )
+
+dataFrame <- melt( avgs )
+dataFrame$scale <- fileData$scale
+
+colnames( dataFrame ) <- c( "ms", "type", "scale" )
+
+dataFrame$type <- as.character( dataFrame$type )
+dataFrame$type <- factor( dataFrame$type, levels=unique( dataFrame$type ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = dataFrame, aes( x = scale, y = ms, fill = type ) )
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Max Number of Intents/Flow Rules" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+
+print( "Generating bar graph bars." )
+width <- 1.3
+barGraphFormat <- geom_bar( stat="identity", position=position_dodge( ), width = width )
+title <- ggtitle( chartTitle )
+result <- fundamentalGraphData + barGraphFormat + title
+
+
+print( paste( "Saving bar chart to", outputFile ) )
+ggsave( outputFile, width = 10, height = 6, dpi = 200 )
+
+print( paste( "Successfully wrote bar chart out to", outputFile ) )
diff --git a/TestON/JenkinsFile/scripts/SCPFswitchLat.R b/TestON/JenkinsFile/scripts/SCPFswitchLat.R
new file mode 100644
index 0000000..9131be9
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/SCPFswitchLat.R
@@ -0,0 +1,172 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# **********************************************************
+# STEP 1: File management.
+# **********************************************************
+
+print( "STEP 1: File management." )
+
+# Command line arguments are read. Args usually include the database filename and the output
+# directory for the graphs to save to.
+# ie: Rscript SCPFgraphGenerator SCPFsampleDataDB.csv ~/tmp/
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL ) # For databases
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 7 ] ) ){
+ print( "Usage: Rscript SCPFswitchLat <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for output graphs include the testname and the graph type.
+# See the examples below. paste() is used to concatenate strings.
+errBarOutputFileUp <- paste( args[ 7 ], "SCPFswitchLat_", sep = "" )
+errBarOutputFileUp <- paste( errBarOutputFileUp, args[ 6 ], sep = "" )
+errBarOutputFileUp <- paste( errBarOutputFileUp, "_UpErrBarWithStack.jpg", sep = "" )
+
+errBarOutputFileDown <- paste( args[ 7 ], "SCPFswitchLat_", sep = "" )
+errBarOutputFileDown <- paste( errBarOutputFileDown, args[ 6 ], sep = "" )
+errBarOutputFileDown <- paste( errBarOutputFileDown, "_DownErrBarWithStack.jpg", sep = "" )
+
+print( "Reading from databases." )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+command <- paste( "SELECT * FROM switch_latency_details WHERE branch = '", args[ 6 ], sep="" )
+command <- paste( command, "' AND date IN ( SELECT MAX( date ) FROM switch_latency_details WHERE branch='", sep = "")
+command <- paste( command, args[ 6 ], sep="" )
+command <- paste( command, "' )", sep="" )
+
+print( paste( "Sending SQL command:", command ) )
+
+fileData <- dbGetQuery( con, command )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "Sorting data." )
+
+upAvgs <- c( fileData[ 'up_device_to_graph_avg' ], fileData[ 'role_reply_to_device_avg' ], fileData[ 'role_request_to_role_reply_avg' ], fileData[ 'feature_reply_to_role_request_avg' ], fileData[ 'tcp_to_feature_reply_avg' ] )
+upAvgsData <- melt( upAvgs )
+upAvgsData$scale <- fileData$scale
+upAvgsData$up_std <- fileData$up_std
+
+colnames( upAvgsData ) <- c( "ms", "type", "scale", "stds" )
+upAvgsData$type <- as.character( upAvgsData$type )
+upAvgsData$type <- factor( upAvgsData$type, levels=unique( upAvgsData$type ) )
+
+downAvgs <- c( fileData[ 'down_device_to_graph_avg' ], fileData[ 'ack_to_device_avg' ], fileData[ 'fin_ack_to_ack_avg' ] )
+downAvgsData <- melt( downAvgs )
+downAvgsData$scale <- fileData$scale
+downAvgsData$down_std <- fileData$down_std
+
+colnames( downAvgsData ) <- c( "ms", "type", "scale", "stds" )
+downAvgsData$type <- as.character( downAvgsData$type )
+downAvgsData$type <- factor( downAvgsData$type, levels=unique( downAvgsData$type ) )
+
+upAvgsData <- na.omit( upAvgsData ) # Omit any data that doesn't exist
+downAvgsData <- na.omit( downAvgsData )
+
+print( "Up Averages Results:" )
+print( upAvgsData )
+
+print( "Down Averages Results:" )
+print( downAvgsData )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+
+print( "Generating fundamental graph data (Switch Up Latency)." )
+width <- 1
+ if ( min( fileData[ 'up_end_to_end_avg' ] - upAvgsData$stds ) < 0 ) {
+ yMin <- min( fileData[ 'up_end_to_end_avg' ] + upAvgsData$stds ) * 1.05
+ } else {
+ yMin <- 0
+ }
+yMax <- max( fileData[ 'up_end_to_end_avg' ] + upAvgsData$stds )
+
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+
+mainPlot <- ggplot( data = upAvgsData, aes( x = scale, y = ms, fill = type, ymin = fileData[ 'up_end_to_end_avg' ] - stds, ymax = fileData[ 'up_end_to_end_avg' ] + stds ) )
+xScaleConfig <- scale_x_continuous( breaks=c( 1, 3, 5, 7, 9) )
+yLimit <- ylim( yMin, yMax )
+xLabel <- xlab( "Scale" )
+yLabel <- ylab( "Latency (ms)" )
+fillLabel <- labs( fill="Type" )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + yLimit + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+print( "Generating bar graph with error bars (Switch Up Latency)." )
+barGraphFormat <- geom_bar( stat="identity", width = width )
+errorBarFormat <- geom_errorbar( width = width )
+
+title <- ggtitle( "Switch Up Latency" )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars (Switch Up Latency) to", errBarOutputFileUp ) )
+ggsave( errBarOutputFileUp, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote bar chart with error bars (Switch Up Latency) out to", errBarOutputFileUp ) )
+
+
+print( "Generating fundamental graph data (Switch Down Latency)." )
+ if ( min( fileData[ 'down_end_to_end_avg' ] - downAvgsData$stds ) < 0 ) {
+ yMin <- min( fileData[ 'down_end_to_end_avg' ] - downAvgsData$stds )
+ } else {
+ yMin <- 0
+ }
+ yMax <- max( fileData[ 'down_end_to_end_avg' ] + downAvgsData$stds )
+
+mainPlot <- ggplot( data = downAvgsData, aes( x = scale, y = ms, fill = type, ymin = fileData[ 'down_end_to_end_avg' ] - stds, ymax = fileData[ 'down_end_to_end_avg' ] + stds ) )
+yLimit <- ylim( yMin, yMax )
+theme <- theme( plot.title=element_text( hjust = 0.5, size = 28, face='bold' ) )
+
+fundamentalGraphData <- mainPlot + yLimit + xScaleConfig + xLabel + yLabel + fillLabel + theme
+
+print( "Generating bar graph with error bars (Switch Down Latency)." )
+barGraphFormat <- geom_bar( stat="identity", width = width )
+errorBarFormat <- geom_errorbar( width = width )
+
+title <- ggtitle( "Switch Down Latency" )
+result <- fundamentalGraphData + barGraphFormat + errorBarFormat + title
+
+
+print( paste( "Saving bar chart with error bars (Switch Down Latency) to", errBarOutputFileDown ) )
+ggsave( errBarOutputFileDown, width = 10, height = 6, dpi = 200 )
+
+
+print( paste( "Successfully wrote bar chart with error bars (Switch Down Latency) out to", errBarOutputFileDown ) )
diff --git a/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R b/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
new file mode 100644
index 0000000..2c7e442
--- /dev/null
+++ b/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
@@ -0,0 +1,156 @@
+# Copyright 2017 Open Networking Foundation (ONF)
+#
+# Please refer questions to either the onos test mailing list at <onos-test@onosproject.org>,
+# the System Testing Plans and Results wiki page at <https://wiki.onosproject.org/x/voMg>,
+# or the System Testing Guide page at <https://wiki.onosproject.org/x/WYQg>
+#
+# TestON is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# TestON is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with TestON. If not, see <http://www.gnu.org/licenses/>.
+#
+# If you have any questions, or if you don't understand R,
+# please contact Jeremy Ronquillo: jeremyr@opennetworking.org
+
+# This is the R script that generates the FUNC and HA result graphs.
+
+# **********************************************************
+# STEP 1: Data management.
+# **********************************************************
+
+print( "STEP 1: Data management." )
+
+# Command line arguments are read. Args include the database credentials, test name, branch name, and the directory to output files.
+print( "Reading commmand-line args." )
+args <- commandArgs( trailingOnly=TRUE )
+
+# Import libraries to be used for graphing and organizing data, respectively.
+# Find out more about ggplot2: https://github.com/tidyverse/ggplot2
+# reshape2: https://github.com/hadley/reshape
+# RPostgreSQL: https://code.google.com/archive/p/rpostgresql/
+print( "Importing libraries." )
+library( ggplot2 )
+library( reshape2 )
+library( RPostgreSQL )
+
+# Check if sufficient args are provided.
+if ( is.na( args[ 8 ] ) ){
+ print( "Usage: Rscript testCaseGraphGenerator.R <database-host> <database-port> <database-user-id> <database-password> <test-name> <branch-name> <#-builds-to-show> <directory-to-save-graphs>" )
+ q() # basically exit(), but in R
+}
+
+# Filenames for the output graph include the testname, branch, and the graph type.
+outputFile <- paste( args[ 8 ], args[ 5 ], sep="" )
+outputFile <- paste( outputFile, args[ 6 ], sep="_" )
+outputFile <- paste( outputFile, args[ 7 ], sep="_" )
+outputFile <- paste( outputFile, "builds", sep="-" )
+outputFile <- paste( outputFile, "_graph.jpg", sep="" )
+
+# From RPostgreSQL
+print( "Reading from databases." )
+con <- dbConnect( dbDriver( "PostgreSQL" ), dbname="onostest", host=args[ 1 ], port=strtoi( args[ 2 ] ), user=args[ 3 ],password=args[ 4 ] )
+
+print( "Creating SQL command." )
+# Creating SQL command based on command line args.
+command <- paste( "SELECT * FROM executed_test_tests WHERE actual_test_name='", args[ 5 ], sep="" )
+command <- paste( command, "' AND branch='", sep="" )
+command <- paste( command, args[ 6 ], sep="" )
+command <- paste( command, "' ORDER BY date DESC LIMIT ", sep="" )
+command <- paste( command, args[ 7 ], sep="" )
+fileData <- dbGetQuery( con, command )
+
+# Title of graph based on command line args.
+title <- paste( args[ 5 ], args[ 6 ], sep=" - " )
+title <- paste( title, "Results of Last ", sep=" \n " )
+title <- paste( title, args[ 7 ], sep="" )
+title <- paste( title, " Builds", sep="" )
+
+# **********************************************************
+# STEP 2: Organize data.
+# **********************************************************
+
+print( "STEP 2: Organize data." )
+
+# Create lists c() and organize data into their corresponding list.
+print( "Sorting data into new data frame." )
+categories <- c( fileData[ 'num_failed' ], fileData[ 'num_passed' ], fileData[ 'num_planned' ] )
+
+# Parse lists into data frames.
+# This is where reshape2 comes in. Avgs list is converted to data frame.
+dataFrame <- melt( categories )
+dataFrame$build <- fileData$build
+colnames( dataFrame ) <- c( "Tests", "Status", "Build" )
+
+# Format data frame so that the data is in the same order as it appeared in the file.
+dataFrame$Status <- as.character( dataFrame$Status )
+dataFrame$Status <- factor( dataFrame$Status, levels=unique( dataFrame$Status ) )
+
+# Add planned, passed, and failed results to the dataFrame (for the fill below the lines)
+dataFrame$num_planned <- fileData$num_planned
+dataFrame$num_passed <- fileData$num_passed
+dataFrame$num_failed <- fileData$num_failed
+
+# Adding a temporary reversed iterative list to the dataFrame so that there are no gaps in-between build numbers.
+dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
+
+dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+
+print( "Data Frame Results:" )
+print( dataFrame )
+
+# **********************************************************
+# STEP 3: Generate graphs.
+# **********************************************************
+
+print( "STEP 3: Generate graphs." )
+
+print( "Creating main plot." )
+# Create the primary plot here.
+# ggplot contains the following arguments:
+# - data: the data frame that the graph will be based off of
+# - aes: the asthetics of the graph which require:
+# - x: x-axis values (usually iterative, but it will become build # later)
+# - y: y-axis values (usually tests)
+# - color: the category of the colored lines (usually status of test)
+theme_set( theme_grey( base_size = 20 ) ) # set the default text size of the graph.
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = Tests, color = Status ) )
+
+print( "Formatting main plot." )
+# geom_ribbon is used so that there is a colored fill below the lines. These values shouldn't be changed.
+failedColor <- geom_ribbon( aes( ymin = 0, ymax = dataFrame$num_failed ), fill = "red", linetype = 0, alpha = 0.07 )
+passedColor <- geom_ribbon( aes( ymin = 0, ymax = dataFrame$num_passed ), fill = "green", linetype = 0, alpha = 0.05 )
+plannedColor <- geom_ribbon( aes( ymin = 0, ymax = dataFrame$num_planned ), fill = "blue", linetype = 0, alpha = 0.01 )
+
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$iterative, label = dataFrame$Build )
+yScaleConfig <- scale_y_continuous( breaks = seq( 0, max( dataFrame$Tests ), by = ceiling( max( dataFrame$Tests ) / 10 ) ) )
+
+xLabel <- xlab( "Build Number" )
+yLabel <- ylab( "Test Cases" )
+fillLabel <- labs( fill="Type" )
+legendLabels <- scale_colour_discrete( labels = c( "Failed", "Passed", "Planned" ) )
+centerTitle <- theme( plot.title=element_text( hjust = 0.5 ) ) # To center the title text
+theme <- theme( plot.title = element_text( size = 28, face='bold' ) )
+
+# Store plot configurations as 1 variable
+fundamentalGraphData <- mainPlot + plannedColor + passedColor + failedColor + xScaleConfig + yScaleConfig + xLabel + yLabel + fillLabel + legendLabels + centerTitle + theme
+
+print( "Generating line graph." )
+
+lineGraphFormat <- geom_line( size = 1.1 )
+pointFormat <- geom_point( size = 3 )
+title <- ggtitle( title )
+
+result <- fundamentalGraphData + lineGraphFormat + pointFormat + title
+
+# Save graph to file
+print( paste( "Saving result graph to", outputFile ) )
+ggsave( outputFile, width = 10, height = 6, dpi = 200 )
+print( paste( "Successfully wrote result graph out to", outputFile ) )