refactor: use execSync for tests (#158)

automl/package.json

@@ -1,30 +1,26 @@
 {
  "name": "@google-cloud/automl-samples",
  "description": "Samples for the Cloud AutoML Client Library for Node.js.",
- "version": "0.0.1",
  "license": "Apache-2.0",
  "author": "Google LLC",
  "engines": {
  "node": ">=8"
  },
+ "files": [
+ "!test/*"
+ ],
  "repository": "googleapis/nodejs-automl",
  "private": true,
- "nyc": {
- "exclude": [
- "**/*.test.js"
- ]
- },
  "scripts": {
  "test": "mocha --timeout 600000"
  },
  "dependencies": {
  "@google-cloud/automl": "^0.2.0",
- "chai": "^4.2.0",
- "execa": "^1.0.0",
  "mathjs": "^5.5.0",
  "yargs": "^13.2.1"
  },
  "devDependencies": {
- "mocha": "^6.0.1"
+ "mocha": "^6.0.1",
+ "chai": "^4.2.0"
  }
 }

automl/test/automlVision.test.js

@@ -17,9 +17,10 @@
 
 const path = require('path');
 const {assert} = require('chai');
-const execa = require('execa');
+const cp = require('child_process');
+
+const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
 
-const exec = async cmd => (await execa.shell(cmd)).stdout;
 const cmdDataset = `node vision/automlVisionDataset.js`;
 const cmdModel = `node vision/automlVisionModel.js`;
 const cmdPredict = `node vision/automlVisionPredict.js`;
@@ -33,49 +34,49 @@ const sampleImage2 = path.join(testImgPath, `testImage2.jpg`);
 describe(`auto ml vision`, () => {
  it.skip(`should create, list, and delete a dataset`, async () => {
  // Check to see that this dataset does not yet exist
- let output = await exec(`${cmdDataset} list-datasets`);
+ let output = execSync(`${cmdDataset} list-datasets`);
  assert.strictEqual(output.includes(testDataSetName), false);
 
  // Create dataset
- output = await exec(`${cmdDataset} create-dataset -n "${testDataSetName}"`);
+ output = execSync(`${cmdDataset} create-dataset -n "${testDataSetName}"`);
  const dataSetId = output
  .split(`\n`)[1]
  .split(`:`)[1]
  .trim();
  assert.match(output, new RegExp(testDataSetName));
 
  // Delete dataset
- output = await exec(`${cmdDataset} delete-dataset -i "${dataSetId}"`);
+ output = execSync(`${cmdDataset} delete-dataset -i "${dataSetId}"`);
  assert.match(output, /Dataset deleted./);
  });
 
  // See : https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/vision/automl/model_test.py
  // We make two models running this test, see hard-coded workaround below
  it.skip(`should create a dataset, import data, and start making a model`, async () => {
  // Check to see that this dataset does not yet exist
- let output = await exec(`${cmdDataset} list-datasets`);
+ let output = execSync(`${cmdDataset} list-datasets`);
  assert.strictEqual(output.includes(dummyDataSet), false);
 
  // Create dataset
- output = await exec(`${cmdDataset} create-dataset -n "${dummyDataSet}"`);
+ output = execSync(`${cmdDataset} create-dataset -n "${dummyDataSet}"`);
  const dataSetId = output
  .split(`\n`)[1]
  .split(`:`)[1]
  .trim();
  assert.match(output, new RegExp(dummyDataSet));
 
  // Import Data
- output = await exec(
+ output = execSync(
  `${cmdDataset} import-data -i "${dataSetId}" -p "gs://nodejs-docs-samples-vcm/flowerTraindata20lines.csv"`
  );
  assert.match(output, /Data imported./);
 
  // Check to make sure model doesn't already exist
- output = await exec(`${cmdModel} list-models`);
+ output = execSync(`${cmdModel} list-models`);
  assert.notMatch(output, new RegExp(testModelName));
 
  // begin training dataset, getting operation ID for next operation
- output = await exec(`
+ output = execSync(`
  ${cmdModel} create-model -i "${dataSetId}" -m "${testModelName}" -t "2"`);
  const operationName = output
  .split(`\n`)[0]
@@ -86,7 +87,7 @@ describe(`auto ml vision`, () => {
  assert.match(output, /Training started.../);
 
  // poll operation status, here confirming that operation is not complete yet
- output = await exec(
+ output = execSync(
  `${cmdModel} get-operation-status -i "${dataSetId}" -o "${operationName}"`
  );
  assert.match(output, /done: false/);
@@ -97,18 +98,16 @@ describe(`auto ml vision`, () => {
  const flowersDisplayName = `flowersTest`;
 
  // Confirm dataset exists
- let output = await exec(`${cmdDataset} list-datasets`);
+ let output = execSync(`${cmdDataset} list-datasets`);
  assert.match(output, new RegExp(flowersDisplayName));
 
  // List model evaluations, confirm model exists
- output = await exec(
+ output = execSync(
  `${cmdModel} list-model-evaluations -a "${flowersModelId}"`
  );
 
  // Display evaluation
- output = await exec(
- `${cmdModel} display-evaluation -a "${flowersModelId}"`
- );
+ output = execSync(`${cmdModel} display-evaluation -a "${flowersModelId}"`);
  assert.match(output, /Model Precision/);
  });
 
@@ -117,23 +116,23 @@ describe(`auto ml vision`, () => {
  const flowersDisplayName = `flowers`;
 
  // Confirm dataset exists
- let output = await exec(`${cmdDataset} list-datasets`);
+ let output = execSync(`${cmdDataset} list-datasets`);
  assert.match(output, new RegExp(flowersDisplayName));
 
  // List model evaluations, confirm model exists
- output = await exec(
+ output = execSync(
  `${cmdModel} list-model-evaluations -a "${donotdeleteModelId}"`
  );
  // Run prediction on 'testImage.jpg' in resources folder
- output = await exec(
+ output = execSync(
  `${cmdPredict} predict -i "${donotdeleteModelId}" -f "${sampleImage2}" -s "0.5"`
  );
  assert.match(output, /dandelion/);
  });
 
  // List datasets
  it(`should list datasets`, async () => {
- const output = await exec(`${cmdDataset} list-datasets`);
+ const output = execSync(`${cmdDataset} list-datasets`);
  assert.match(output, /List of datasets:/);
  });
 });