diff --git a/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R b/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
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+# 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 ) )