This repository contains an R package for "Directed Random" research paper. The devtools development framework was used to implement this R package. The content of this repository are the following:

• R functions that read data sets, preform data manipulations, statistically analyse the results, and generate tables and plots.
• Data sets of our experiment.
• Unit tests to mitigate the risk of our results.

The following instructions will get you a copy of the project up and running on your local R environment. This method is ideal if you plan to leverage, without modification, our existing functions and data sets.

Please note that these instructions have been tested on an Ubuntu 16.04 LTS workstation running the following version of R:

R version 3.3.3 (2017-03-06) -- "Another Canoe"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

You would need devtools to install our package:

• devtools - Tools to make an R developer's life easier

You can type the next command in your R development environment if you want to install this package:

devtools::install_github("schemaanalyst/directedRandom-R-pkg")

Within your R environment, use the following commands:

• Reading data sets into dataframes:

mutants <- directedRandomR::read_mutants()
analysis <- directedRandomR::read_analysis()

• Generating Tables (Latex or dataframes):

x1 <- directedRandomR::table_generator_timing(analysis, rtrn = "data", m = "mean")
x2 <- directedRandomR::table_generator_coverage(analysis, rtrn = "data", m = "mean")
x3 <- directedRandomR::table_generator_mutation_score(mutants, rtrn = "data", m = "mean")
x4 <- directedRandomR::table_generator_mutant_operators(mutants, rtrn = "data", m = "mean")

NOTE: The rtrn parameter is used to return either a dataframe (rtrn = "data") or a latex table (rtrn = "tex"). The m parameter is used to print the table as median or mean results.

• For more information about our functions please type this:

library(directedRandomR)
?read_mutants
?read_analysis()
?table_generator_timing()
?table_generator_coverage()
?table_generator_mutation_score()
?table_generator_mutant_operators()

If you are interested in extending this package with new data sets and your own functions, then you can run the following command to first clone this repository:

git clone https://github.com/schemaanalyst/directedRandom-R-pkg.git

Furthermore, in your R environment, you can run each of the following commands. This will help you to build, install, load, and test our R packages using devtools:

devtools::document()
devtools::install()
devtools::load_all()
devtools::test()

Whilst running our tests, the following results will appear:

coverages-sample-size: ....
coverages-median-resutlts: ....
coverages-effect-size: ...
coverages-u-test: ...
mutant-operators-sample-size: ........
mutant-operators-mean-results: ............
mutant-operators-u-test-results: ...........
mutant-operators-effect-size-results: ...........
mutant-scores-mean-results: ....
mutant-scores-sample-size: ....
mutant-scores-u-test-results: ...
mutant-scores-effect-size-results: ...
table-generator: ........
testgenerationtiming-sample-size: ....
testgenerationtiming-median-results: ....
testgenerationtiming-u-test-results: ...
testgenerationtiming-effect-size-results: ...

DONE =========================================

The conventions we have used are explained in the following URLs:

• R Packages - A site for “R packages” development. It was published with O’Reilly in April 2015.
• README template.

• devtools - Tools to make an R developer's life easier
• roxygen2 - In-source documentation for R
• Check The DESCRIPTION file - To review all the dependencies

• Abdullah Alsharif - Initial work - aalshrif90
• Gregory M. Kapfhammer - Quality Assurance - gkapfham
• Phil McMinn - Quality Assurance - philmcminn