Suppose you have some rules that operate on a data payload that can have many possible combinations of data values. During testing how can you be sure that you have covered all the possibilities? Including all the possible ways the data can be bad? Running your test against production data probably won’t help. Even though you may have millions of records, chances are you won’t find every possible combination of the data.
So is there a way that Corticon could help to generate test data?
Yes, Of Course!
Let’s imagine we have some business rules that evaluate meals at a restaurant.
A meal consists of a starter, an entrée, two sides, dessert and coffee
For example here are some possible meals
Starter is Soup or Salad
Entrée is Chicken or Beef
Sides are Fries, Peas, Carrots, Broccoli
Dessert is Ice Cream or Soufflé
Coffee is Regular or Decaf
Here are a few possible meals
soup, chicken, broccoli, fries, ice cream, decaf
salad, chicken, broccoli, fries, ice cream, regular
soup, chicken, carrots, fries, no dessert, regular
soup, chicken, broccoli, fries, ice cream, regular
salad, beef, broccoli, fries, ice cream, decaf
soup, chicken, broccoli, carrots, soufflé, decaf
If we also know the price (or calories) of each of the meal components we can compute the total cost (calories) of each meal.
Perhaps we want to determine which meal is cheapest or has least calories