#379 Add unit tests to check pyproject.toml velocity template Python …

foundation/foundation-mda/src/test/java/com/boozallen/aiops/mda/generator/PyprojectGenerationSteps.java

@@ -0,0 +1,119 @@
+package com.boozallen.aiops.mda.generator;/*-
+ * #%L
+ * aiSSEMBLE::Foundation::MDA
+ * %%
+ * Copyright (C) 2021 Booz Allen
+ * %%
+ * This software package is licensed under the Booz Allen Public License. All Rights Reserved.
+ * #L%
+ */
+
+import com.boozallen.aiops.mda.generator.util.PipelineUtils;
+
+import com.boozallen.aiops.mda.metamodel.element.*;
+import com.boozallen.aiops.mda.metamodel.element.StepElement;
+import com.boozallen.aiops.mda.metamodel.element.RecordElement;
+import io.cucumber.java.Before;
+import io.cucumber.java.Scenario;
+import io.cucumber.java.en.Given;
+import io.cucumber.java.en.Then;
+import io.cucumber.java.en.When;
+import org.apache.commons.io.FileUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.technologybrewery.fermenter.mda.GenerateSourcesHelper;
+import org.technologybrewery.fermenter.mda.element.ExpandedProfile;
+
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+public class PyprojectGenerationSteps extends AbstractModelInstanceSteps {
+    private static final Logger logger = LoggerFactory.getLogger(PyprojectGenerationSteps.class);
+    private RecordElement record;
+
+    @Before("@pyproject-generation")
+    public void setup(Scenario scenario) throws IOException {
+        this.scenario = scenario.getName();
+        FileUtils.deleteDirectory(GENERATED_METADATA_DIRECTORY);
+    }
+
+    @Given("a project with the name {string}")
+    public void a_project_with_the_name(String projectName) throws IOException {
+        createProject(projectName, "shared");
+    }
+
+    @Given("a {string} pipeline using the {string} implementation")
+    public void a_pipeline_using_the_implementation(String pipelineType, String pipelineImplementation) throws IOException {
+        String pipelineName = pipelineType + "-pipeline";
+        createAndSavePipeline(pipelineName, pipelineType, pipelineImplementation);
+    }
+
+    @Given("a {string} pipeline with step type {string} using the {string} implementation")
+    public void a_pipeline_with_step_type_using_the_implementation(String pipelineType, String pipelineStepType, String pipelineImplementation) throws IOException {
+        String pipelineName = pipelineType + "-pipeline";
+        String pipelineStepName = pipelineStepType + "-step";
+
+        createAndSavePipeline(pipelineName, pipelineType, pipelineImplementation, pipeline -> {
+            try {
+                StepElement pipelineStep = createPipelineStep(pipelineStepName, pipelineStepType);
+                pipeline.addStep(pipelineStep);
+            } catch (Exception e) {
+                logger.error("Failed to add new step to test pipeline", e);
+            };
+
+            });
+    }
+
+    @Given("a record model with a corresponding dictionary type defined")
+    public void a_record_model_with_a_corresponding_dictionary_type_defined() throws Exception {
+        createSampleDictionaryType();
+        record = new RecordElement();
+        record.setName("TestRecord");
+        record.setPackage(BOOZ_ALLEN_PACKAGE);
+        saveRecordToFile(record);
+    }
+
+    @When("the {string} profile {string} is generated")
+    public void the_profile_is_generated(String profileType, String profile) throws Exception {
+        readMetadata(projectName);
+        Map<String, ExpandedProfile> profiles = loadProfiles();
+        GenerateSourcesHelper.performSourceGeneration(profile, profiles, this::createGenerationContext, (missingProfile, foundProfiles) -> {
+            throw new RuntimeException("Missing profile: " + missingProfile);
+        }, new Slf4jDelegate(logger), projectDir.toFile());
+    }
+
+    @Then("the {string} is generated with the minimum Python version, {string}")
+    public void the_is_generated_with_the_minimum_Python_version(String outputFile, String minPythonVersion) throws IOException {
+        File fullFilepath = new File(projectDir.toString(), outputFile);
+
+        assertTrue("The " + outputFile + " file was not generated!", fullFilepath.exists());
+
+        AtomicBoolean minPythonVersionFound = fileInspector(fullFilepath, "python = \"" + minPythonVersion+ "\"");
+
+        assertTrue("The minimum Python version " + minPythonVersion + " was NOT found in " + fullFilepath + ".", minPythonVersionFound.get());
+    }
+
+    private AtomicBoolean fileInspector(File filepath, String patternToFind) throws IOException {
+        AtomicBoolean patternFound = new AtomicBoolean(false);
+        Files.lines(Paths.get(filepath.getAbsolutePath())).forEach(line -> {
+            if(line.contains(patternToFind)) {
+                patternFound.set(true);
+            }
+        });
+        return patternFound;    
+    }
+
+    private static StepElement createPipelineStep(String stepName, String stepType) throws IOException {
+        StepElement step = new StepElement();
+        step.setName(stepName);
+        step.setType(stepType);
+        return step;
+    }
+}

foundation/foundation-mda/src/test/resources/specifications/pyproject-generation.feature

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+@pyproject-generation
+Feature: Minimum Python version can be set in the pyproject.toml.vm files.
+
+  @pipeline-generation @code-generation
+  Scenario: The data-flow pipeline generates the correct pyproject.toml
+    Given a project with the name "example"
+    And a "data-flow" pipeline using the "data-delivery-pyspark" implementation
+    When the "pipeline" profile "data-delivery-pyspark-pipeline" is generated
+    Then the "pyproject.toml" is generated with the minimum Python version, ">=3.8"
+
+   @pipeline-generation @code-generation
+   Scenario Outline: The record metamodel with profile "<profile>" generates the correct pyproject.toml
+    Given a project with the name "example"
+    And a record model with a corresponding dictionary type defined
+    When the "metadata" profile "<profile>" is generated
+    Then the "pyproject.toml" is generated with the minimum Python version, ">=3.8"
+
+    Examples:
+      | profile                                    |
+      | data-delivery-combined-data-records-python |
+      | data-delivery-data-records-core-python     |
+      | data-delivery-data-pyspark                 |
+
+
+  @pipeline-generation @code-generation
+  Scenario Outline: The machine-learning pipeline with the "<pipelineStepType>" step generates the correct pyproject.toml
+    Given a project with the name "example"
+    And a "machine-learning" pipeline with step type "<pipelineStepType>" using the "machine-learning-mlflow" implementation
+    When the "pipeline" profile "<profile>" is generated
+    Then the "pyproject.toml" is generated with the minimum Python version, ">=3.8"
+
+    Examples:
+      | pipelineStepType   | profile                             |
+      | training           | machine-learning-training           |
+      | sagemaker-training | machine-learning-sagemaker-training | 
+      | inference          | machine-learning-inference          |