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Testing

This section explains how to set up and run integration tests for bookmarks, provides samples for custom integration tests, and outlines unit tests.

There are a number of unit and integration tests across the project in each of the modules.

Integration Tests

By default, the integration tests will not get executed when you build the project, as they have a severe impact on build time.

There is a separate Maven profile called IT that you can run with the following command when you want to execute the integration tests:

mvn install -P IT.

Bookmark Tests

The application uses a test harness library which has been developed to regression test Atoti Server projects. This library enables us to start up a cube, execute MDX queries and assert the values match.

By default, the Atoti Server instance will load data from /src/test/resources/data.

Creating Bookmark (MDX) Tests

  1. Start up your Atoti Server instance.

  2. Run the class com.activeviam.mr.regression.bookmark.creator.BookmarkTestCreatorRunner - This will create a set of files in the folder with MDX queries in /src/test/resources/test-bookmarks. All the MDX queries will be stored in files ending with .query.

  3. Stop your Atoti Server instance.

Creating/Replacing Expected Results

Run the class com.activeviam.mr.regression.ReferenceRegressionTestIT as a Java application - This will start up an Atoti Server instance, execute the queries in /src/test/resources/test-bookmarks and store the results in files ending with .csv.

Executing Bookmark (MDX) Tests

Run the class com.activeviam.mr.regression.ReferenceRegressionTestIT as a JUnit test - This will start up an Atoti Server instance, execute the queries in the files ending with .query and assert the results against the corresponding file ending with .csv.

Custom Integration Tests

Integration tests can be defined fully in code, this includes things such as sample data being injected into the Atoti Server instance, the query being executed together with the expected values.

There are a number of sample integration tests in the reference implementation that have been defined as examples.

Example implementations of this class can be found in the reference implementation.

Unit Tests

By default, unit tests are executed when you run mvn install. Some example tests can be found in the MR application module, where datastores have been created and mocked in order to test certain functionalities.

Sensitivity module measure testing

The sensitivity module provides an abstract base class for unit tests, called SensiMeasureTestConfig. Extending this class allows a unit test to wire all non-replaceable beans and beans that can be replaced via qualified @Primary custom beans. Postprocessor injections are handled in a @BeforeEach method, a Cube tested object is created and an abstract data() method is provided.

A test extending this class will only require store, reference, and measure chain, and dimension configuration to be imported, alongside any beans required by the calculations. The overridden data() method will supply the required data to the Cube, and individual @Test methods can run queries against the provided context object.

A sample test using this configuration is provided. The following is the test class definition:

@ContextConfiguration(classes = {
        // Datastores
        FxRate.class,
        MarketDataSet.class,
        TradeSensitivities.class,
        MarketData.class,
        SensiLadders.class,
        CorporateAction.class,
        DynamicTenors.class,

        // Dimensions
        DateDimensionConfig.class,
        MarketDataDimensionConfig.class,
        RiskDimensionConfig.class,
        DynamicBucketingDimensionConfig.class,
        CurrencyDimensionConfig.class,
        SensitivitiesDimensionConfig.class,
        BookingDimensionConfig.class,

        // Measure parameters
        BaseParametersConfig.class,
        DeltaParametersConfig.class,

        // Measure chains
        BaseSensitivityChain.class,
        BasePnLExplainChain.class,
        DeltaNativeCurrencyChain.class,
        DeltaLadderExpansionChain.class,
        DeltaCurrentDateMarketDataChain.class,
        DeltaPreviousDateMarketDataChain.class,
        DeltaNextDateMarketDataChain.class,
        DeltaPnLExplainChain.class,

        // Formulas
        PnLExplainFormulaProviderConfig.class
}, loader = AnnotationConfigContextLoader.class)
@Tag("Scalar")
@TestPropertySource(properties = {
        "formula.rule.delta.commodity=Relative,1,1,true",
        "formula.rule.delta.girr=Absolute,1,1,true",
        "formula.rule.delta.csr-non-sec=Absolute,1,1,true",
        "configuration.data-model.scalar-sensitivities=true"
})
class DeltaPnLExplainScalarPostProcessorTest extends SensiMeasureTestConfig {
}

Several stores, all references and most measure chains available in the application are excluded from this configuration, providing a minimal cube configuration that allows the calculation of Delta PnL Explain values. Some of the imported dimensions contain levels or hierarchies that would be impossible to create without additional stores being imported, however the base configuration uses a DimensionFilter object to ignore unbuildable dimensions.

As it is a scalar sensitivity test, the property is set to true, and PnL Explain formula properties are also set.

Data needs to be added to all stores impacting the tested calculations, using the SimpleTransactionBuilder object:

    @Override
    public ITransactionsBuilder data() {
        return SimpleTransactionBuilder.start()
        .inStore("yourStoreName")
        .add(tuple, with, all, correct, values)
        .inStore("aDifferentStore")
        .add(different, tuple)
        .end()
    }

All store additions defined in this test will be applied to the stores imported in the configuration.

Queries can then be run using the CubeTester API through the getContext() method:

   @Test
    void testDeltaLadderPnLExplain() {
        getContext()
                .mdxQuery("SELECT NON EMPTY { [Measures].[Delta PnL Explain] } ON COLUMNS, NON EMPTY [Risk].[Risk Factors].[RiskFactor].Members ON ROWS FROM ( SELECT [Risk].[Risk Factors].[ALL].[AllMember].[BRL.OIS_Implied yield] ON COLUMNS FROM [Sensitivity Cube])WHERE [Dates].[Date].[AsOfDate].[2018-09-20]")
                .getTester()
                .hasOnlyOneCell()
                .containingFormattedValue("17,576,250.00");
    }