Crate test_data_generation

The are multiple ways to use the Test Data Generation library. It all depends on your intent.

Profile

The easiest way is to use a Profile. The profile module provides functionality to create a profile on a data sample (Strings). Once a profile has been made, data can be generated by calling the pre_generate() and generate() functions, in that order.

extern crate test_data_generation;

use test_data_generation::Profile;

fn main() {
    // analyze the dataset
	let mut data_profile =  Profile::new();

    // analyze the dataset
	data_profile.analyze("Smith, John");
	data_profile.analyze("Doe, John");
	data_profile.analyze("Dale, Danny");
	data_profile.analyze("Rickets, Ronney");

    // confirm 4 data samples were analyzed
   	assert_eq!(data_profile.patterns.len(), 4);

    // prepare the generator
    data_profile.pre_generate();

    // generate some data
   	println!("The generated name is {:?}", data_profile.generate());
}

You can also export (archive as JSON file) the profile for later use. This allows for the algorithm to be retrieved without having to store the actual data that was analyzed.

extern crate test_data_generation;

use test_data_generation::Profile;

fn main() {
	//create a profile and analyze some data
	let mut old_profile =  Profile::new();
	old_profile.analyze("Smith, John");
	old_profile.analyze("O'Brian, Henny");
	old_profile.analyze("Dale, Danny");
	old_profile.analyze("Rickets, Ronney");

	old_profile.pre_generate();

	//save the profile for later
	assert_eq!(old_profile.save("./tests/samples/sample-00-profile").unwrap(), true);

	// create a new profile from the archive json file
	let mut new_profile = Profile::from_file("./tests/samples/sample-00-profile");

	// generate some data. NOTE that the pre-generate() was already called prior to saving
    println!("The generated name is {:?}", new_profile.generate());
}

Data Sample Parser

If you are using CSV files of data samples, then you may wish to use a Data Sample Parser. The data_sample_parser module provides functionality to read sample data, parse and analyze it, so that test data can be generated based on profiles.

extern crate test_data_generation;
use test_data_generation::data_sample_parser::DataSampleParser;

fn main() {
    let mut dsp = DataSampleParser::new();
    dsp.analyze_csv_file(&String::from("./tests/samples/sample-01.csv")).unwrap();

    println!("My new name is {} {}", dsp.generate_record()[0], dsp.generate_record()[1]);
    // My new name is Abbon Aady
}

You can also save the Data Sample Parser (the algorithm) as an archive file (json) ...

extern crate test_data_generation;
use test_data_generation::data_sample_parser::DataSampleParser;

fn main() {
    let mut dsp =  DataSampleParser::new();
    dsp.analyze_csv_file(&String::from("./tests/samples/sample-01.csv")).unwrap();

    assert_eq!(dsp.save(&String::from("./tests/samples/sample-01-dsp")).unwrap(), true);
}

and use it at a later time.

extern crate test_data_generation;
use test_data_generation::data_sample_parser::DataSampleParser;

fn main() {
    let mut dsp = DataSampleParser::from_file(&String::from("./tests/samples/sample-01-dsp"));

	println!("Sample data is {:?}", dsp.generate_record()[0]);
}

You can also generate a new csv file based on the data sample provided.

extern crate test_data_generation;

use test_data_generation::data_sample_parser::DataSampleParser;

fn main() {
    let mut dsp =  DataSampleParser::new();

   	dsp.analyze_csv_file(&String::from("./tests/samples/sample-01.csv")).unwrap();
   	dsp.generate_csv(100, &String::from("./tests/samples/generated-01.csv")).unwrap();
}

Modules

configs	The configs module provides functionality for the library to read configuration settings that the user can set in their implementation.
data_sample_parser	The data_sample_parser module provides functionality to read sample data, parse and analyze it, so that test data can be generated based on profiles.
engine	Fact
macros
shared

Macros

levenshtein_distance	This macro calculates the levenshtein distance between 2 strings. See: https://crates.io/crates/levenshtein
random_between	This macro generates a random number for a given range. Returns a u32.
random_percentage	This macro generates a random number between 0 and 100. Returns a f64.
realistic_test	This function calculates the percent difference between 2 strings.

Structs

Profile

Represents a Profile for sample data that has been analyzed and can be used to generate realistic data