Data Model Tests


Functional testing is an important part of data model development. Errors in the model can have major consequences in the correct behaviour of software that is developed using the model. ALFA has a number of features where logic can be expressed - constraints, data quality rules, library functions etc. These need to be tested to ensure their correct behaviour.

To aid with functional testing, ALFA includes built-in support to define tests alongside model definitions. The testing capabilities will be extended to include test coverage reports to ensure that users have a complete picture on completeness of model testing.

Having very high or full test coverage should be an assurance to data model consumers of the completeness and correctness of the model and rules expressed within. Naturally, these tests can form part of regression testing.

Testing Areas

Models expressed in ALFA have several areas that can be of interest for testing. This list is not exhaustive, but highlights some areas worth considering for data model testing.

  1. Creating a new user-defined-type.
    • E.g. Valid numeric values, valid enumeration constants
  2. Valid data-type constraints.
    • E.g. Valid string size, valid collection size, valid value ranges.
    • string(3,3), list<int>(1,*), int(0,100)
  3. Validate correct assert behaviour.
  4. Validate library function behaviour.
  5. Correct service function operation.
  6. Date, time values far in the past or future, or ‘edge’ values - 00:00, 23:59:59:999, leap years etc.
  7. Handing unexpected values.

Support for Testing

Tests are authored using 2 newly introduced features in ALFA.

  1. A new built-in service that executes tests.
  2. A new test user-defined-type.

1. Test Scenario service

The new alfa.test.Scenario service exposes number of methods that can be used to compose tests.

// This service is passed into methods that are part of a 'test' user-defined-type. Number of method are available to
// formula data required for a test, and execute functionality and assert expected results.
service alfa.test.Scenario() {

    // The specified entity object is available within the test to be accessed via the 'query' or 'lookup' functions.
    given( description : string, data : $entity ) : void

    // Load JSON files from the path specified (file, or directory), and those entity objects are available within the
    // test to be accessed via the 'query' or 'lookup' functions.
    givenAll( description : string, path : string ) : void

    // The specified service is available in the context of this Scenario as a mocked service.
    // When invoked, returns a random value from the results list.
    with( description : string, srv : $service, results : map< FunctionName : string, ReturnValues : list< string > > ) : void

    // When the testBody is executed, no error is expected and completes normally
    pass( description : string, testBody : func<(), void > ) : void

    // When the testBody is executed, an error is expected.
    // This method should be used when the exact error message expected can be implementation specific (E.g.Java, Python etc)
    fail( description : string, testBody : func<(), void > ) : void

    // When the testBody is executed, an error is expected on the field specified by the 'errorFieldPath' field
    failOn( description : string, testBody : func<(), void >, expectedErrorFieldPath : string ) : void

    // When the testBody is executed, an error is expected with the message being reported
    failWith( description : string, testBody : func<(), void >, expectedErrorMessage : string ) : void

    // Assert the lambda returns true
    assertTrue( description : string, testBody : func<(), boolean > ) : void

    // Create a random object of the given type
    random( typeName : string ) : $udt

Note the testBody argument in testBody : func<(), void > is a lambda expression that is executed to validate the behaviour of the test. This can be a single expression or a code block as { ... }.

2. Test Definition

A testcase definition allow tests to be expressed as a ‘Feature’ containing ‘Scenarios’ containing predefined conditions and expected behaviour.

A testcase definition is syntactically identical to a library. All functions within a testcase accept one parameter of type alfa.test.Scenario. The Scenario service methods are invoked to assert the expected behaviour.

testcase NameOfTestFeature {
    testScenario1( s : alfa.test.Scenario ) : void {
        // invoke methods on argument 's' to perform testing

    testScenario2( s : alfa.test.Scenario ) : void {
        // invoke methods on argument 's' to perform testing

Note the alfa.test.Scenario object is valid only for the duration of the method. Each method call will have a new instance of the Scenario object created.

Users are free to choose a naming strategy for tests. For improving coverage, a testcase per user-defined-type can be followed as a convention. Tools to enforce availability of tests and test coverage expectations are currently being developed.

Example Test

testcase FeatureTestDemo {
    divideByZero( s : alfa.test.Scenario ) : void {
        // Assert that 10 divide 0 causes a failure
        s::fails("Divide by zero", ( ) => MathLib::divide( 10, 0 ) )

    validatePerson( s : alfa.test.Scenario ) : void {
        // Assert a new Person object can be created
        s::pass("A Person object can be created", ( ) =>  new Example.Person( "Bob", 20 )  )

        // Assert a new Person with age set to 150 fails ( due to constraints set )
        s::failsWith("Person object age constraint upto 120", ( ) =>  new Person("Bob", 150 ), "Invalid age 150" )

Upcoming Testing Features

As the core support for ALFA Tests is nearing completion, the following features will be introduced in future releases.

  1. The ALFA Randomiser will be extended to overwrite certain fields of interest for testing, reducing code for writing tests.
  2. A new `ALFA Test Coverage’ Tool will enable specifying what type of coverage is expected and provide reports on current coverage.
  3. IDE Integration - the Test Runner and Coverage Tool will be integrated to the ALFA Editor plugin.
  4. A new ‘Performance Test Data’ Tool - using ALFA Randomiser with hints to generate large samples of data that can be used simulate large volumes of data.