[ONOS-7175]: Refractor R Scripts that generate wiki result graphs in TestON.
- Code is "chunked"; blocks of code are sectioned into sub-steps.
- Many comments have been added and updated.
- Many console messages have been added and updated.
Change-Id: I86853b4a3917d807e634311b672ab6d6d57b1194
diff --git a/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R b/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
index 2973755..f8ec145 100644
--- a/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
+++ b/TestON/JenkinsFile/scripts/testCaseGraphGenerator.R
@@ -26,72 +26,137 @@
# STEP 1: Data management.
# **********************************************************
+print( "**********************************************************" )
print( "STEP 1: Data management." )
+print( "**********************************************************" )
# 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/
+# ----------------
+# Import Libraries
+# ----------------
+
print( "Importing libraries." )
library( ggplot2 )
library( reshape2 )
library( RPostgreSQL )
-# Check if sufficient args are provided.
+# -------------------
+# Check CLI Arguments
+# -------------------
+
+print( "Verifying CLI args." )
+
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>" )
+
+ print( paste( "Usage: Rscript testCaseGraphGenerator.R",
+ "<database-host>",
+ "<database-port>",
+ "<database-user-id>",
+ "<database-password>",
+ "<test-name>", # part of the output filename
+ "<branch-name>", # for sql and output filename
+ "<#-builds-to-show>", # for sql and output filename
+ "<directory-to-save-graphs>",
+ sep=" " ) )
+
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="" )
+# -------------------------------
+# Create Title and Graph Filename
+# -------------------------------
-# 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 title of graph." )
-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="" )
+title <- paste( args[ 5 ],
+ " - ",
+ args[ 6 ],
+ " \n Results of Last ",
+ args[ 7 ],
+ " Builds",
+ sep="" )
+
+print( "Creating graph filename." )
+
+outputFile <- paste( args[ 8 ],
+ args[ 5 ],
+ "_",
+ args[ 6 ],
+ "_",
+ args[ 7 ],
+ "-builds_graph.jpg",
+ sep="" )
+
+# ------------------
+# SQL Initialization
+# ------------------
+
+print( "Initializing SQL" )
+
+con <- dbConnect( dbDriver( "PostgreSQL" ),
+ dbname = "onostest",
+ host = args[ 1 ],
+ port = strtoi( args[ 2 ] ),
+ user = args[ 3 ],
+ password = args[ 4 ] )
+
+# ---------------------
+# Test Case SQL Command
+# ---------------------
+print( "Generating Test Case SQL command." )
+
+command <- paste( "SELECT * FROM executed_test_tests WHERE actual_test_name='",
+ args[ 5 ],
+ "' AND branch='",
+ args[ 6 ],
+ "' ORDER BY date DESC LIMIT ",
+ args[ 7 ],
+ sep="" )
+
+print( "Sending SQL command:" )
+print( command )
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." )
+print( "**********************************************************" )
+print( "STEP 2: Organize Data." )
+print( "**********************************************************" )
-# 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' ] )
+# -------------------------------------------------------
+# Combining Passed, Failed, and Planned Data
+# -------------------------------------------------------
-# Parse lists into data frames.
-# This is where reshape2 comes in. Avgs list is converted to data frame.
+print( "Combining Passed, Failed, and Planned Data." )
+
+categories <- c( fileData[ 'num_failed' ],
+ fileData[ 'num_passed' ],
+ fileData[ 'num_planned' ] )
+
+# --------------------
+# Construct Data Frame
+# --------------------
+
+print( "Constructing data frame from combined data." )
+
dataFrame <- melt( categories )
+
+# Rename column names in dataFrame
+colnames( dataFrame ) <- c( "Tests",
+ "Status" )
+
+# Add build dates to the dataFrame
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$Status ) )
+dataFrame$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
@@ -101,7 +166,8 @@
# Adding a temporary reversed iterative list to the dataFrame so that there are no gaps in-between build numbers.
dataFrame$iterative <- rev( seq( 1, nrow( fileData ), by = 1 ) )
-dataFrame <- na.omit( dataFrame ) # Omit any data that doesn't exist
+# Omit any data that doesn't exist
+dataFrame <- na.omit( dataFrame )
print( "Data Frame Results:" )
print( dataFrame )
@@ -110,7 +176,13 @@
# STEP 3: Generate graphs.
# **********************************************************
-print( "STEP 3: Generate graphs." )
+print( "**********************************************************" )
+print( "STEP 3: Generate Graph." )
+print( "**********************************************************" )
+
+# -------------------
+# Main Plot Generated
+# -------------------
print( "Creating main plot." )
# Create the primary plot here.
@@ -120,40 +192,111 @@
# - x: x-axis values (usually iterative, but it will become build # later)
# - y: y-axis values (usually tests)
# - color: the category of the colored lines (usually status of test)
-theme_set( theme_grey( base_size = 26 ) ) # set the default text size of the graph.
-mainPlot <- ggplot( data = dataFrame, aes( x = iterative, y = Tests, color = Status ) )
+
+mainPlot <- ggplot( data = dataFrame, aes( x = iterative,
+ y = Tests,
+ color = Status ) )
+
+# -------------------
+# Main Plot Formatted
+# -------------------
print( "Formatting main plot." )
+
# geom_ribbon is used so that there is a colored fill below the lines. These values shouldn't be changed.
-failedColor <- geom_ribbon( aes( ymin = 0, ymax = dataFrame$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 )
+failedColor <- geom_ribbon( aes( ymin = 0,
+ ymax = dataFrame$num_failed ),
+ fill = "red",
+ linetype = 0,
+ alpha = 0.07 )
-colors <- scale_color_manual( values=c( "#E80000", "#00B208", "#00A5FF") )
+passedColor <- geom_ribbon( aes( ymin = 0,
+ ymax = dataFrame$num_passed ),
+ fill = "green",
+ linetype = 0,
+ alpha = 0.05 )
-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 ) ) )
+plannedColor <- geom_ribbon( aes( ymin = 0,
+ ymax = dataFrame$num_planned ),
+ fill = "blue",
+ linetype = 0,
+ alpha = 0.01 )
+
+# Colors for the lines
+lineColors <- scale_color_manual( values=c( "#E80000", # red
+ "#00B208", # green
+ "#00A5FF") ) # blue
+
+# ------------------------------
+# Fundamental Variables Assigned
+# ------------------------------
+
+print( "Generating fundamental graph data." )
+
+theme_set( theme_grey( base_size = 26 ) ) # set the default text size of the graph.
+
+xScaleConfig <- scale_x_continuous( breaks = dataFrame$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 Cases", "Passed Cases", "Planned Cases" ) )
-centerTitle <- theme( plot.title=element_text( hjust = 0.5 ) ) # To center the title text
-theme <- theme( plot.title = element_text( size = 32, face='bold' ), axis.text.x = element_text( angle = 0, size = 14 ), legend.position="bottom", legend.text=element_text( size=22 ), legend.title = element_blank(), legend.key.size = unit( 1.5, 'lines' ) )
+imageWidth <- 15
+imageHeight <- 10
+imageDPI <- 200
+
+legendLabels <- scale_colour_discrete( labels = c( "Failed Cases",
+ "Passed Cases",
+ "Planned Cases" ) )
+
+# Set other graph configurations here.
+theme <- theme( plot.title = element_text( hjust = 0.5, size = 32, face ='bold' ),
+ axis.text.x = element_text( angle = 0, size = 14 ),
+ legend.position = "bottom",
+ legend.text = element_text( size = 22 ),
+ legend.title = element_blank(),
+ legend.key.size = unit( 1.5, 'lines' ) )
+
+title <- ggtitle( title )
# Store plot configurations as 1 variable
-fundamentalGraphData <- mainPlot + plannedColor + passedColor + failedColor + xScaleConfig + yScaleConfig + xLabel + yLabel + fillLabel + colors + legendLabels + centerTitle + theme
+fundamentalGraphData <- mainPlot +
+ plannedColor +
+ passedColor +
+ failedColor +
+ xScaleConfig +
+ yScaleConfig +
+ xLabel +
+ yLabel +
+ lineColors +
+ legendLabels +
+ theme +
+ title
+
+# ----------------------------
+# Generating Line Graph Format
+# ----------------------------
print( "Generating line graph." )
lineGraphFormat <- geom_line( size = 1.1 )
pointFormat <- geom_point( size = 3 )
-title <- ggtitle( title )
-result <- fundamentalGraphData + lineGraphFormat + pointFormat + title
+result <- fundamentalGraphData +
+ lineGraphFormat +
+ pointFormat
-# Save graph to file
+# -----------------------
+# Exporting Graph to File
+# -----------------------
+
print( paste( "Saving result graph to", outputFile ) )
-ggsave( outputFile, width = 15, height = 10, dpi = 200 )
-print( paste( "Successfully wrote result graph out to", outputFile ) )
+
+ggsave( outputFile,
+ width = imageWidth,
+ height = imageHeight,
+ dpi = imageDPI )
+
+print( paste( "[SUCCESS] Successfully wrote result graph out to", outputFile ) )