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 ) )