Data Generators and Fakers

Often, you don’t want to generate totally random data; it suffices that some aspects of it are random. This naturally raises the question: Where can one get non-random, natural data from, and how can one integrate this into Fandango?

Augmenting Grammars with Data

The straightforward solution would be to simply extend our grammar with more natural data. In order to obtain more natural first and last names in our ongoing names/age example, for instance, we could simply extend the persons.fan rule

<first_name> ::= <name>

to

These are the given names on Pablo Picasso’s birth certificate.

<first_name> ::= <name> | "Alice" | "Bob" | "Eve" | "Pablo Diego José Francisco de Paula Juan Nepomuceno Cipriano de la Santísima Trinidad"

and extend the rule

<last_name> ::= <name>

to, say,

<last_name> ::= <name> | "Doe" | "Smith" | "Ruiz Picasso"

then we can have Fandango create names such as

Pablo Diego José Francisco de Paula Juan Nepomuceno Cipriano de la Santísima Trinidad Smith,301
Yliiwd Doe,29197
Eve Smith,98
Pablo Diego José Francisco de Paula Juan Nepomuceno Cipriano de la Santísima Trinidad Doe,5298
Kqgirq Smith,934
Pablo Diego José Francisco de Paula Juan Nepomuceno Cipriano de la Santísima Trinidad Smith,16
Bob Smith,3449
Ls Smith,09
Eve Ahnvu,05437
Pablo Diego José Francisco de Paula Juan Nepomuceno Cipriano de la Santísima Trinidad Smith,954

Note that we still get a few “random” names; this comes as specified by our rules. By default, Fandango picks each alternative with equal likelihood, so there is a 20% chance for the first name and a 25% chance for the last name to be completely random.

Note

Future Fandango versions will have means to control these likelihoods.

Using Fakers

Frequently, there already are data sources available that you’d like to reuse – and converting each of their elements into a grammar alternative is inconvenient. That is why Fandango allows you to specify a data source as part of the grammar - as a Python function that supplies the respective value. Let us illustrate this with an example.

The Python faker module is a great source of “natural” data, providing “fake” data for names, addresses, credit card numbers, and more.

Here’s an example of how to use it:

from faker import Faker
fake = Faker()
for i in range(10):
  print(fake.first_name())

Mark
Jerry
Benjamin
Laurie
Angela
Thomas
Kayla
Kristen
Michael
Cheryl

Have a look at the faker documentation to see all the fake data it can produce. The methods first_name() and last_name() are what we need. The idea is to extend the <first_name> and <last_name> rules such that they can draw on the faker functions. To do so, in Fandango, you can simply extend the grammar as follows:

A generator applies to all alternatives, not just the last one.

<first_name> ::= <name> := fake.first_name()

:= is the assignment operator in several programming languages; in Python, it can be used to assign values within expressions.

The generator := EXPR assigns the value produced by the expression EXPR (in our case, fake.first_name()) to the symbol on the left-hand side of the rule (in our case, <first_name>).

Caution

Whatever value the generator returns, it must be parseable by at least one of the alternatives in the rule. Our example works because <first_name> matches the format of fake.first_name().

Tip

If your generator returns a string, a “match-all” rule such as

<generated_string> ::= <char>* := generator()

will fit all possible string values returned by generator().

We can do the same for the last name, too; and then this is the full Fandango spec persons-faker.fan:

from faker import Faker
fake = Faker()

include('persons.fan')

<first_name> ::= <name> := fake.first_name()
<last_name> ::= <name> := fake.last_name()

Note

The Fandango include() function includes the Fandango definitions of the given file. This way, we need not repeat the definitions from persons.fan and only focus on the differences.

Note

Python code (from Python files) that you use in a generator (or in a constraint, for that matter) needs to be imported. Use the Python import features to do that.

Attention

include(FILE) is for Fandango files, import MODULE is for Python modules.

This is what the output of the above spec looks like:

Tristan Price,5
Sarah Estes,924
Clinton Griffin,03
Alyssa Rivera,48
Timothy Hoffman,45
Michael Reid,01
Dustin Jones,15
Erin Salazar,1
Kristin Wolf,17
Stephanie Smith,90

You see that all first and last names now stem from the Faker library.

Number Generators

In the above output, the “age” fields are still very random, though. With generators, we can achieve much more natural distributions.

After importing the Python random module:

import random

we can make use of dozens of random number functions to use as generators. For instance, random.randint(A, B) return a random integer \(n\) such that \(A \le n \le B\) holds. To obtain a range of ages between 25 and 35, we can thus write:

<age> ::= <digit>+ := str(random.randint(25, 35));

Warning

All Fandango generators must return strings or byte strings.

• Use str(N) to convert a number N into a string

• Use bytes([N]) to convert numbers N into bytes.

The resulting Fandango spec file produces the desired range of ages:

Michael Johnson,33
Anthony Pennington,25
Sarah Baker,31
Janice Johnson,25
Joseph Wright,31
Angela Adams,31
Tracy Russell,27
James Lang,31
Emily Sims,28
Kelly Collins,32

We can also create a Gaussian (normal) distribution this way:

<age> ::= <digit>+ := str(int(random.gauss(35)));

random.gauss() returns floating point numbers. However, the final value must fit the given symbol rules (in our case, <digit>+), so we convert the age into an integer (int()).

These are the ages we get this way:

Veronica Aguilar,33
Kristen Jordan,36
David Harmon,34
Nicholas Rodriguez,35
Jonathan Reid,34
Tina Wright,36
Phillip Dunn,34
Michael Barker,34
Dana Gonzalez,34
Anthony Shaw,33

In Statistical Distributions, we will introduce more ways to obtain specific distributions.

Generators and Random Productions

In testing, you want to have a good balance between common and uncommon inputs:

• Common inputs are important because they represent the typical usage, and you don’t want your program to fail there;

• Uncommon inputs are important because they uncover bugs you may not find during alpha or beta testing, and thus avoid latent bugs (and vulnerabilities!) slipping into production.

We can easily achieve such a mix by adding rules such as

<first_name> ::= <name> | <natural_name>
<natural_name> ::= <name> := fake.first_name()

With this, both random names (<name>) and natural names (<natural_name>) will have a chance of 50% to be produced:

Ijawk Bass,06
Nhtg Bailey,0
Shannon White,93
Pi Garrett,5793
Izz Johnson,869
Rawy Velazquez,52
Urf Russo,165
Christopher Green,3
Xisv Park,9801
Rlyof Brown,406

Combining Generators and Constraints

When using a generator, why does one still have to specify the format of the data, say <name>? This is so for two reasons:

To allow access to the elements of the generator output, Fandango parses the output according to the symbol rules.

1. It allows the Fandango spec to be used for parsing existing data, and consequently, mutating it;

2. It allows additional constraints to be applied on the generator result and its elements.

In our example, the latter can be used to further narrow down the set of names. If we want all last names to start with an S, for instance, we can invoke Fandango as

Grammar symbols <...> support all Python string methods.

$ fandango fuzz -f persons-faker.fan -c '<last_name>.startswith("S")' -n 10

and we get

Victoria Smith,99
Mary Shaw,540
Joseph Stewart,28
Thomas Smith,3
Robin Spencer,7
Cindy Smith,620
Amber Smith,498
Matthew Smith,15
Shelby Sanchez,65
Joseph Stewart,27

When to use Generators, and when Constraints

One might assume that instead of a generator, one could also use a constraint to achieve the same effect. So, couldn’t one simply add a constraint that says

<first_name> == fake.first_name()

In case this should work, this is only through some internal Fandango optimization that directly assigns computed values to symbols.

Unfortunately, this does not work.

The reason is that the faker returns a different value every time it is invoked, making it hard for Fandango to solve the constraint. Remember that Fandango solves constraints by applying mutations to a population, getting closer to the target with each iteration. If the target keeps on changing, the algorithm will lose guidance and will not progress towards the solution.

Likewise, in contrast to our example in Combining Generators and Constraints, one may think about using a constraint to set a limit to a number, say:

$ fandango fuzz -f persons-faker.fan -c 'str(<last_name>).startswith("S")' -c 'int(<age>) >= 25 and int(<age>) <= 35' -n 10

This would work:

Angela Smith,33
Andrea Stout,26
Marcus Stevens,25
Brooke Solis,25
Kevin Schneider,027
Debra Simmons,25
Charles Smith,33
Melissa Stevens,25
Brooke Stevens,25
Marcus Solis,25

But while the values will fit the constraint, they will not be randomly distributed. This is because Fandango treats and generates them as strings (= sequences of digits), ignoring thur semantics as numerical values. To obtain well-distributed numbers from the beginning, use a generator.

1. If a value to be produced is random, it should be added via a generator.

2. If a value to be produced is constant, it can go into a generator or a constraint.

3. If a value to be produced must be part of a valid input, it should go into a constraint. (Constraints are checked during parsing and production.)