![](\"docs/images/GitLabCISchedule-1.png\")
](docs/images/GitLabCISchedule-1.png)\n\n[![](\"docs/images/GitLabCISchedule-2.png\")
](docs/images/GitLabCISchedule-2.png)\n\n## **Test Reporting**\nWe have setup this project to make use of a few different types of reporting to show off what is possible.\n\nThe reporting comes in these forms:\n* CSV File that summarizes results of utterance tests\n* Reporting via AWS Cloudwatch\n* Reporting via DataDog\n\nEach is discussed in more detail below.\n\n### **CSV File**\nThe CSV File contains the following output:\n\n| Column | Description |\n| --- | --- |\n| name | The name of the receipt to ask for\n| transcript | The actual response back from Alexa\n| success | Whether or not the test was successful\n| expectedResponses | The possible expected response back from the utterance\n\n### **DataDog**\nDataDog captures metrics related to how all the tests have performed. 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]() \nThis project enables batch testing of utterances for voice experiences.\n\nIt leverages Bespoken's Virtual Devices to run large sets of utterances through Alexa, Google Assistant, and other voice platforms.\n\n## **Getting Started**\n### **Installation**\n**This package requires Node.js 10 or greater.**\n\nTo install the Bespoken Batch Tester, just run: \n```\nnpm install bespoken-batch-tester --save\n```\n\nWe recommend creating a new project to store artifacts related to the tester, such as the testing configuration file, CI configuration, and custom source code.\n\n### **Environment Management**\nWe use dotenv when running locally, which takes environment variables from a local `.env` file.\n\nTo set this up, just make a copy of `example.env` and name it `.env`. Replace the values inside there with the correct values for your configuration.\n\nFor running with continuous integration (such as Jenkins, Circle CI or Gitlab), these values should instead be set as actual values within the CI environment.\n\n### **Virtual Device Setup**\n* Create a virtual device with our [easy-to-follow guide here](https://read.bespoken.io/end-to-end/setup/#creating-a-virtual-device).\n* Add them the configuration file, as described below\n\nIf you want to use multiple tokens, potentially for different purposes, leverage tags:\n```json\n{\n \"virtualDevices\": {\n \"myToken\": {\n \"tags\": [\"USAccount\"]\n },\n \"myOtherToken\": {\n \"tags\": [\"UKAccount\"],\n \"settings\": {\n \"listener.maxDuration\": 5\n }\n }\n }\n}\n```\n\nThe tags can then be assigned to a record with [record.addDeviceTag](https://bespoken.gitlab.io/batch-tester/Record.html#addDeviceTag):\n```\nrecord.addDeviceTag('USAccount')\n```\nOnly tokens that have that tag (or tags) will be used to process it.\n\nAdditionally, per device settings can be set via the `settings` property. This allows for overriding the behavior of the device as needed.\n\nFor more information on the best practices for virtual device management, [read our guide here]([docs/ACCOUNT_SETUP.md]).\n\n### **Create a Configuration File**\nHere is a bare minimum configuration file:\n```json\n{\n \"job\": \"utterance-tester\",\n \"sequence\": [\"open my audio player\"],\n \"source\": \"csv-source\",\n \"sourceFile\": \"path/to/my/file.csv\",\n \"virtualDevices\": {\n \"myVirtualDevice\": { \"tags\": [\"my-optional-tags\"]}\n }\n}\n```\n\nTo get started, cut and paste those settings into a new file, such as `batch-test.json`.\n\nMore information on configuring the batch test is below.\n\n### **Source Data**\nSource data by default comes from a CSV file (by default, we look at `input/records.csv`).\n\nThe structure of the file is:\n\n| Field | Required | Description|\n| --- | --- | --- |\n| utterance | Yes | The utterance to be said to the device\n| device | No | The device value corresponds to the tag set on the device. If this value matches a tag, that device will be eligible to process this utterance.\n| [expected values] | No | There can zero-to-many expected fields defined - these will automatically be compared to fields on the JSON output\n\n#### *device* column\nWith regard to the `device` column, if we have two devices, like so:\n```\n \"virtualDevices\": {\n \"device1\": { \"tags\": [\"google\"]},\n \"device2\": { \"tags\": [\"alexa\"]}\n }\n```\n\nIf our record looks like this:\n```csv\nutterance,device,transcript\nplay despacito,alexa,playing the song you requested\n```\n\nThe record will only be run with the device2, as it has the tag `alexa` that corresponds to our DEVICE value.\n\n#### *expected value* columns\nThe expected field values will automatically be compared to the actual value on the response from the device.\n\nFor example, if we have a record like this:\n```csv\nutterance,transcript\nplay despacito,playing the song you requested\n```\n\nOur response from the virtual device may look like this:\n```\n{\n \"transcript\": \"I don't know that one\"\n}\n```\n\nThe actual transcript value will be compared to the expected one, and the test will be marked a failure or success based on a partial match comparison (i.e., the actual value must include the expected value though they do not need to be an exact match).\n\nMore complex field expressions can be handled with the `fields` configuration property, [described below](#fields).\n\n### **Running the Tester**\nOnce the configuration file is created, just enter:\n```\nbbt process batch-test.json\n```\n\nAnd it will be off and running. In practice, we recommend this not be run locally but in a CI environment.\n\nThe tester will create a results.csv file, as well as publish metrics to the configured metrics provider.\n\n## **In-Depth Configuration**\nThe environment variables store sensitive credentials.\n\nOur configuration file stores information particular to how the tests should run, but of a non-sensitive nature.\n\nAn example file:\n```json\n{\n \"fields\": {\n \"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"\n },\n \"interceptor\": \"./src/my-interceptor\",\n \"job\": \"utterance-tester\",\n \"saveInterval\": 300,\n \"limit\": 5,\n \"maxAttempts\": 3,\n \"metrics\": \"datadog-metrics\",\n \"sequence\": [\"open my audio player\"],\n \"sequential\": false,\n \"source\": \"csv-source\",\n \"sourceFile\": \"path/to/my/file.csv\",\n \"transcript\": true,\n \"virtualDevices\": {\n \"VIRTUAL_DEVICE_1\": {\n \"tags\": [\"tag1\", \"tag2\"]\n }\n },\n \"virtualDeviceConfig\": {\n \"phoneNumber\": \"+1 XXX XXX XXX\",\n \"twilio_speech_timeout\": 2,\n \"twilio_timeout\": 10,\n \"waitTimeInterval\": 2000,\n \"maxWaitTime\": 300000 \n }\n}\n```\n\nEach of the properties is explained below:\n### **`fields`**\nEach field represents a value in the JSON response. It will be added to the result output.\n\nIf the field also is a column in the CSV file, then the value in the CSV is compared to the value in the actual response. \n\nThe fields can be mapped to complex JSON path values, such as: \n`\"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"`\n\nThe JSON path will be applied to the actual result from the response. If more than one value matches the JSON path expression, \nthen if ANY of the actual values matches the expected the test will pass.\n\n### **`interceptor`**\nThe interceptor allows for the core behavior of the batch runner to be modified.\n\nThere are five main methods: \n* interceptRecord - Called before the record is processed\n* interceptResult - Called before the result is finalized\n* interceptPreProcess - Called after the records have been loaded but before any of them is processed\n* interceptPostProcess - Called after all the records have been executed\n* interceptRequest - Called before the request is sent to a virtual device\n\nUsing [interceptRecord](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptRecord), changes can be made to the utterance or the meta data of a record before it is used in a test.\n\nUsing [interceptResult](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptResult), changes can be made to the result of processing. This can involve:\n* Adding tags to the result (for use in metrics displays)\n* Changing the `success` flag based on custom validation logic\n* Adding output fields to the CSV output to provide additional information to report readers\n\nUsing `interceptPreProcess` custom code can be called before starting the execution of the records. This can involve:\n* Setting up a local storage\n* Calling an API\nBe aware that this function will be called when resuming too.\n\nUsing `interceptPostProcess` custom code can be called after the execution of the records. This can involve:\n* Calling an API\n* Saving objects to a local storage.\n\nUsing `interceptRequest` the request can be modified before calling the virtual device. This can involve:\n* Adding more messages (before or after the main one)\n* Adding phrases for better speech recognition\n* Doing operations related to the virtual device about to be used\n\nYou can read all about the Interceptor class here:\nhttps://bespoken.gitlab.io/batch-tester/Interceptor.html\n\n### **`saveInterval`**\nTime interval where the batch job is saved. It's represented in seconds.\n\nThis defaults to 300.\n\n### **`limit`**\nThe numbers of records to test during test execution. Very useful when you want to try just a small subset of utterances.\n\n### **`maxAttempts`**\nThe number of attempts to try if a request has errors. Defaults to 3.\n\n### **`metrics`**\nWe have builtin two classes for metrics: `datadog-metrics` and `cloudwatch-metrics`.\n\nThis dictates where metrics on the results of the tests are sent.\n\nAdditionally, new metric providers can be used by implementing this base class: \nhttps://bespoken.gitlab.io/batch-tester/Metrics.html\n\n### **`sequence`**\nFor tests in which there are multiple steps required before we do the \"official\" utterance that is being tested, we can specify them here.\n\nTypically, this would involve launching a skill before saying the specific utterance we want to test, but more complex sequences are possible.\n\n### **`sequential`**\nSetting this to true forces records to be processed one after another and not in parallel, regardless of the number of virtual devices that are configured.\n\nThis defaults to false.\n\n### **`source`**\nThe source for records. Defaults to `csv-source`. Additional builtin option is `s3-source`.\n\nFor the `csv-source`, the source file defaults to `input/records.csv`. This can be overridden by setting the `sourceFile` property: \n```\n{ \n \"sourceFile\n}\n```\n\nFor the `s3-source`, a sourceBucket must be set. Additionally, AWS credentials must be set in the environment that can access this bucket.\n\nTo implement your own custom source, [read the API docs](https://bespoken.gitlab.io/batch-tester/Source.html).\n\n### **`transcript`**\nIf set to false, speech-to-text is not performed on the audio response from the device\n\n### **`virtualDevices`**\nSee the section above for information on [configuring virtual devices](#virtual-device-setup).\n\n### **`virtualDeviceBaseURL`**\nFor values other than the default (`https://virtual-device.bespoken.io`), set this property.\n\n### **`virtualDeviceConfig`**\nAllows setting properties to all virtual devices. For example, when using Twilio Virtual Devices, setting the `phone_number` to call.\n\n## Advanced Execution\n### Resuming A Job\nTo resume a job that did not complete, due to errors or timeout, simply set the `RUN_KEY` environment variable.\n\nThe run key can be found in the logs for any run - it will appear like this: \n```\nBATCH SAVE completed key: 7f6113df3e2af093f095d2d3b2505770d9af1c057b93d0dff378d83c0434ec61\n```\n\nThe environment variable can be set locally with:\n```\nexport RUN_KEY=![](\"docs/images/pinoJs.png\")
\n\nTo set a log level, simply set the following env variable: `export LOG_LEVEL=debug`. If not set, `info` is assumed.\n\nWe can also colorize the outputs by setting the env variable `COLORIZE`.\n\n![](\"docs/images/pinoJsColorized.png\")
\n\nTimestamps can be added to the outputs by setting the env variable `DISPLAY_LOG_TIME`.\n\n![](\"docs/images/pinoJsTimestamp.png\")
\n\nFinally, logs can be saved to a file by setting the env variable `SAVE_LOG_FILE`. The file will be placed in the `output` folder and named `batch-tester.log`. It uses pino.js default format.\n\n## **DataDog Configuration**\nFollow [this guide](./docs/datadog#how-to-signup-and-get-an-api-key) to get started.\n\n## **Gitlab Configuration**\nThe gitlab configuration is defined by the file `.gitlab-ci.yml`. The file looks like this:\n```yaml\nimage: node:10\n\ncache:\n paths:\n - node_modules/\n\nutterance-tests:\n script:\n - npm install\n - npm test\n artifacts:\n when: always\n paths:\n - test_output/report/index.inline.html\n - test-report.xml\n reports:\n junit: test-report.xml\n only: \n - schedules\n - web\n```\n\nWhen the GitLab Runner is executed, it takes this file and creates a Linux instance with Node.js, executes the commands under the `script` element, and saves the reports as artifacts.\n\n#### **Setting a schedule**\nIt is very easy to run your end-to-end tests regularly using GitLab. Once your CI file (`.gitlab-ci.yml`) has been uploaded to the repository just go to \"CI/CD => Schedules\" from the left menu. Creating a new schedule looks like this:\n\n[](docs/images/GitLabCISchedule-1.png)\n\n[](docs/images/GitLabCISchedule-2.png)\n\n## **Test Reporting**\nWe have setup this project to make use of a few different types of reporting to show off what is possible.\n\nThe reporting comes in these forms:\n* CSV File that summarizes results of utterance tests\n* Reporting via DataDog\n* Reporting via SQLite and Metabase\n\nEach is discussed in more detail below.\n\n### **CSV File**\nThe CSV File contains the following output:\n\n| Column | Description |\n| --- | --- |\n| name | The name of the receipt to ask for\n| transcript | The actual response back from Alexa\n| success | Whether or not the test was successful\n| expectedResponses | The possible expected response back from the utterance\n\n### **DataDog**\nDataDog captures metrics related to how all the tests have performed. Each time we run the tests, and when `datadog` has been set as the `metric` mechanism to use in the `config.json` file, we push the result of each test to DataDog.\n\nIn general, we are using next metrics:\n- `utterance.success`\n- `utterance.failure`\n\nThe metrics can be easily reported on through a DataDog Dashboard. They also can be used to setup notifcations when certain conditions are triggered.\n\nRead more about configuring DataDog in our [walkthrough](./docs/datadog.md).\n\n### **MySQL and Metabase**\nMySQL allows for reporting and querying via SQL. 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]() \r\nThis project enables batch testing of utterances for voice experiences.\r\n\r\nIt leverages Bespoken's Virtual Devices to run large sets of utterances through Alexa, Google Assistant, and other voice platforms.\r\n\r\n## **Getting Started**\r\n### **Installation**\r\n**This package requires Node.js 10 or greater.**\r\n\r\nTo install the Bespoken Batch Tester, just run: \r\n```\r\nnpm install bespoken-batch-tester --save\r\n```\r\n\r\nWe recommend creating a new project to store artifacts related to the tester, such as the testing configuration file, CI configuration, and custom source code.\r\n\r\n### **Environment Management**\r\nWe use dotenv when running locally, which takes environment variables from a local `.env` file.\r\n\r\nTo set this up, just make a copy of `example.env` and name it `.env`. Replace the values inside there with the correct values for your configuration.\r\n\r\nFor running with continuous integration (such as Jenkins, Circle CI or Gitlab), these values should instead be set as actual values within the CI environment.\r\n\r\n### **Virtual Device Setup**\r\n* Create a virtual device with our [easy-to-follow guide here](https://read.bespoken.io/end-to-end/setup/#creating-a-virtual-device).\r\n* Add them the configuration file, as described below\r\n\r\nIf you want to use multiple tokens, potentially for different purposes, leverage tags:\r\n```json\r\n{\r\n \"virtualDevices\": {\r\n \"myToken\": {\r\n \"tags\": [\"USAccount\"]\r\n },\r\n \"myOtherToken\": {\r\n \"tags\": [\"UKAccount\"],\r\n \"settings\": {\r\n \"listener.maxDuration\": 5\r\n }\r\n }\r\n }\r\n}\r\n```\r\n\r\nThe tags can then be assigned to a record with [record.addDeviceTag](https://bespoken.gitlab.io/batch-tester/Record.html#addDeviceTag):\r\n```\r\nrecord.addDeviceTag('USAccount')\r\n```\r\nOnly tokens that have that tag (or tags) will be used to process it.\r\n\r\nAdditionally, per device settings can be set via the `settings` property. This allows for overriding the behavior of the device as needed.\r\n\r\nFor more information on the best practices for virtual device management, [read our guide here]([docs/ACCOUNT_SETUP.md]).\r\n\r\n### **Create a Configuration File**\r\nHere is a bare minimum configuration file:\r\n```json\r\n{\r\n \"job\": \"utterance-tester\",\r\n \"sequence\": [\"open my audio player\"],\r\n \"source\": \"csv-source\",\r\n \"sourceFile\": \"path/to/my/file.csv\",\r\n \"virtualDevices\": {\r\n \"myVirtualDevice\": { \"tags\": [\"my-optional-tags\"]}\r\n }\r\n}\r\n```\r\n\r\nTo get started, cut and paste those settings into a new file, such as `batch-test.json`.\r\n\r\nMore information on configuring the batch test is below.\r\n\r\n### **Source Data**\r\nSource data by default comes from a CSV file (by default, we look at `input/records.csv`).\r\n\r\nThe structure of the file is:\r\n\r\n| Field | Required | Description|\r\n| --- | --- | --- |\r\n| utterance | Yes | The utterance to be said to the device\r\n| device | No | The device value corresponds to the tag set on the device. If this value matches a tag, that device will be eligible to process this utterance.\r\n| locale | No | The locale to use for this record. If not set, defaults to 'en-US'\r\n| voiceID | No | The voice ID to use for this record. If not set, defaults to 'en-US-Wavenet-D'\r\n| [expected values] | No | There can zero-to-many expected fields defined - these will automatically be compared to fields on the JSON output\r\n\r\n#### *device* column\r\nWith regard to the `device` column, if we have two devices, like so:\r\n```\r\n \"virtualDevices\": {\r\n \"device1\": { \"tags\": [\"google\"]},\r\n \"device2\": { \"tags\": [\"alexa\"]}\r\n }\r\n```\r\n\r\nIf our record looks like this:\r\n```csv\r\nutterance,device,transcript\r\nplay despacito,alexa,playing the song you requested\r\n```\r\n\r\nThe record will only be run with the device2, as it has the tag `alexa` that corresponds to our DEVICE value.\r\n\r\n#### *expected value* columns\r\nThe expected field values will automatically be compared to the actual value on the response from the device.\r\n\r\nFor example, if we have a record like this:\r\n```csv\r\nutterance,transcript\r\nplay despacito,playing the song you requested\r\n```\r\n\r\nOur response from the virtual device may look like this:\r\n```\r\n{\r\n \"transcript\": \"I don't know that one\"\r\n}\r\n```\r\n\r\nThe actual transcript value will be compared to the expected one, and the test will be marked a failure or success based on a partial match comparison (i.e., the actual value must include the expected value though they do not need to be an exact match).\r\n\r\nMore complex field expressions can be handled with the `fields` configuration property, [described below](#fields).\r\n\r\n### **Running the Tester**\r\nOnce the configuration file is created, just enter:\r\n```\r\nbbt process batch-test.json\r\n```\r\n\r\nAnd it will be off and running. In practice, we recommend this not be run locally but in a CI environment.\r\n\r\nThe tester will create a results.csv file, as well as publish metrics to the configured metrics provider.\r\n\r\n## **In-Depth Configuration**\r\nThe environment variables store sensitive credentials.\r\n\r\nOur configuration file stores information particular to how the tests should run, but of a non-sensitive nature.\r\n\r\nAn example file:\r\n```json\r\n{\r\n \"fields\": {\r\n \"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"\r\n },\r\n \"interceptor\": \"./src/my-interceptor\",\r\n \"job\": \"utterance-tester\",\r\n \"saveInterval\": 300,\r\n \"limit\": 5,\r\n \"maxAttempts\": 3,\r\n \"backoffTime\": 10,\r\n \"metrics\": \"datadog-metrics\",\r\n \"sequence\": [\"open my audio player\"],\r\n \"sequential\": false,\r\n \"source\": \"csv-source\",\r\n \"sourceFile\": \"path/to/my/file.csv\",\r\n \"transcript\": true,\r\n \"virtualDevices\": {\r\n \"VIRTUAL_DEVICE_1\": {\r\n \"tags\": [\"tag1\", \"tag2\"]\r\n }\r\n },\r\n \"virtualDeviceConfig\": {\r\n \"phoneNumber\": \"+1 XXX XXX XXX\",\r\n \"twilio_speech_timeout\": 2,\r\n \"twilio_timeout\": 10,\r\n \"waitTimeInterval\": 2000,\r\n \"maxWaitTime\": 300000 \r\n }\r\n}\r\n```\r\n\r\nEach of the properties is explained below:\r\n### **`fields`**\r\nEach field represents a value in the JSON response. It will be added to the result output.\r\n\r\nIf the field also is a column in the CSV file, then the value in the CSV is compared to the value in the actual response. \r\n\r\nThe fields can be mapped to complex JSON path values, such as: \r\n`\"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"`\r\n\r\nThe JSON path will be applied to the actual result from the response. If more than one value matches the JSON path expression, \r\nthen if ANY of the actual values matches the expected the test will pass.\r\n\r\n### **`interceptor`**\r\nThe interceptor allows for the core behavior of the batch runner to be modified.\r\n\r\nThere are six main methods:\r\n* interceptRecord - Called before the record is processed\r\n* interceptResult - Called before the result is finalized\r\n* interceptError - Called when the process has an error after the max attempts\r\n* interceptPreProcess - Called after the records have been loaded but before any of them is processed\r\n* interceptPostProcess - Called after all the records have been executed\r\n* interceptRequest - Called before the request is sent to a virtual device\r\n\r\nUsing [interceptRecord](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptRecord), changes can be made to the utterance or the meta data of a record before it is used in a test.\r\n\r\nUsing [interceptResult](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptResult), changes can be made to the result of processing. This can involve:\r\n* Adding tags to the result (for use in metrics displays)\r\n* Changing the `success` flag based on custom validation logic\r\n* Adding output fields to the CSV output to provide additional information to report readers\r\n\r\nUsing `interceptError` custom code can be called after the max attempts were executed.\r\n\r\nUsing `interceptPreProcess` custom code can be called before starting the execution of the records. This can involve:\r\n* Setting up a local storage\r\n* Calling an API\r\nBe aware that this function will be called when resuming too.\r\n\r\nUsing `interceptPostProcess` custom code can be called after the execution of the records. This can involve:\r\n* Calling an API\r\n* Saving objects to a local storage.\r\n\r\nUsing `interceptRequest` the request can be modified before calling the virtual device. This can involve:\r\n* Adding more messages (before or after the main one)\r\n* Adding phrases for better speech recognition\r\n* Doing operations related to the virtual device about to be used\r\n\r\nYou can read all about the Interceptor class here:\r\nhttps://bespoken.gitlab.io/batch-tester/Interceptor.html\r\n\r\n### **`saveInterval`**\r\nTime interval where the batch job is saved. It's represented in seconds.\r\n\r\nThis defaults to 300.\r\n\r\n### **`limit`**\r\nThe numbers of records to test during test execution. Very useful when you want to try just a small subset of utterances.\r\n\r\n### **`maxAttempts`**\r\nThe number of attempts to try if a request has errors. Defaults to 3.\r\n\r\n### **`backoffTime`**\r\nThe time in seconds to wait before trying again. Defaults to 10.\r\n\r\n### **`metrics`**\r\nWe have builtin two classes for metrics: `datadog-metrics` and `cloudwatch-metrics`.\r\n\r\nThis dictates where metrics on the results of the tests are sent.\r\n\r\nAdditionally, new metric providers can be used by implementing this base class: \r\nhttps://bespoken.gitlab.io/batch-tester/Metrics.html\r\n\r\n### **`sequence`**\r\nFor tests in which there are multiple steps required before we do the \"official\" utterance that is being tested, we can specify them here.\r\n\r\nTypically, this would involve launching a skill before saying the specific utterance we want to test, but more complex sequences are possible.\r\n\r\n### **`sequential`**\r\nSetting this to true forces records to be processed one after another and not in parallel, regardless of the number of virtual devices that are configured.\r\n\r\nThis defaults to false.\r\n\r\n### **`source`**\r\nThe source for records. Defaults to `csv-source`. Additional builtin option is `s3-source`.\r\n\r\nFor the `csv-source`, the source file defaults to `input/records.csv`. This can be overridden by setting the `sourceFile` property: \r\n```\r\n{ \r\n \"sourceFile\r\n}\r\n```\r\n\r\nFor the `s3-source`, a sourceBucket must be set. Additionally, AWS credentials must be set in the environment that can access this bucket.\r\n\r\nTo implement your own custom source, [read the API docs](https://bespoken.gitlab.io/batch-tester/Source.html).\r\n\r\n### **`transcript`**\r\nIf set to false, speech-to-text is not performed on the audio response from the device\r\n\r\n### **`virtualDevices`**\r\nSee the section above for information on [configuring virtual devices](#virtual-device-setup).\r\n\r\n### **`virtualDeviceBaseURL`**\r\nFor values other than the default (`https://virtual-device.bespoken.io`), set this property.\r\n\r\n### **`virtualDeviceConfig`**\r\nAllows setting properties to all virtual devices. For example, when using Twilio Virtual Devices, setting the `phone_number` to call.\r\n\r\n## Advanced Execution\r\n### Resuming A Job\r\nTo resume a job that did not complete, due to errors or timeout, simply set the `RUN_KEY` environment variable.\r\n\r\nThe run key can be found in the logs for any run - it will appear like this: \r\n```\r\nBATCH SAVE completed key: 7f6113df3e2af093f095d2d3b2505770d9af1c057b93d0dff378d83c0434ec61\r\n```\r\n\r\nThe environment variable can be set locally with:\r\n```\r\nexport RUN_KEY=\r\n\r\nTo set a log level, simply set the following env variable: `export LOG_LEVEL=debug`. If not set, `info` is assumed.\r\n\r\nWe can also colorize the outputs by setting the env variable `COLORIZE`.\r\n\r\n\r\n\r\nTimestamps can be added to the outputs by setting the env variable `DISPLAY_LOG_TIME`.\r\n\r\n\r\n\r\nFinally, logs can be saved to a file by setting the env variable `SAVE_LOG_FILE`. The file will be placed in the `output` folder and named `batch-tester.log`. It uses pino.js default format.\r\n\r\n## Type of Utterances\r\nThe record class accepts three types of utterances: text, URL and local audio files.\r\n\r\n| Device | Support |\r\n| --- | --- |\r\n| Alexa | Text, URL, Local Audio File |\r\n| Google Assistant | Text, URL, Local Audio File |\r\n| Test Robot | Text, URL, Local Audio File |\r\n| IVR | Text, URL |\r\n\r\n### Text\r\n```javascript\r\n const record = new Record('play a radio station')\r\n```\r\n\r\n### URL\r\n```javascript\r\n const record = new Record('https://play.radio.com/1234.wav')\r\n```\r\n\r\n### Local audio file\r\nThe audio file path should start from the root path of your project\r\n```\r\nproject/\r\n|-subfolder/\r\n |- audios/\r\n |- radio123.wav\r\n|- src/\r\n |- source.js\r\n```\r\n```javascript\r\n const record = new Record('./subfolder/audio/radio123.wav')\r\n```\r\n\r\n## **DataDog Configuration**\r\nFollow [this guide](./docs/datadog#how-to-signup-and-get-an-api-key) to get started.\r\n\r\n## **Gitlab Configuration**\r\nThe gitlab configuration is defined by the file `.gitlab-ci.yml`. The file looks like this:\r\n```yaml\r\nimage: node:10\r\n\r\ncache:\r\n paths:\r\n - node_modules/\r\n\r\nutterance-tests:\r\n script:\r\n - npm install\r\n - npm test\r\n artifacts:\r\n when: always\r\n paths:\r\n - test_output/report/index.inline.html\r\n - test-report.xml\r\n reports:\r\n junit: test-report.xml\r\n only: \r\n - schedules\r\n - web\r\n```\r\n\r\nWhen the GitLab Runner is executed, it takes this file and creates a Linux instance with Node.js, executes the commands under the `script` element, and saves the reports as artifacts.\r\n\r\n#### **Setting a schedule**\r\nIt is very easy to run your end-to-end tests regularly using GitLab. Once your CI file (`.gitlab-ci.yml`) has been uploaded to the repository just go to \"CI/CD => Schedules\" from the left menu. Creating a new schedule looks like this:\r\n\r\n[](docs/images/GitLabCISchedule-1.png)\r\n\r\n[](docs/images/GitLabCISchedule-2.png)\r\n\r\n## **Test Reporting**\r\nWe have setup this project to make use of a few different types of reporting to show off what is possible.\r\n\r\nThe reporting comes in these forms:\r\n* CSV File that summarizes results of utterance tests\r\n* Reporting via DataDog\r\n* Reporting via SQLite and Metabase\r\n\r\nEach is discussed in more detail below.\r\n\r\n### **CSV File**\r\nThe CSV File contains the following output:\r\n\r\n| Column | Description |\r\n| --- | --- |\r\n| name | The name of the receipt to ask for\r\n| transcript | The actual response back from Alexa\r\n| success | Whether or not the test was successful\r\n| expectedResponses | The possible expected response back from the utterance\r\n\r\n### **DataDog**\r\nDataDog captures metrics related to how all the tests have performed. Each time we run the tests, and when `datadog` has been set as the `metric` mechanism to use in the `config.json` file, we push the result of each test to DataDog.\r\n\r\nIn general, we are using next metrics:\r\n- `utterance.success`\r\n- `utterance.failure`\r\n\r\nThe metrics can be easily reported on through a DataDog Dashboard. They also can be used to setup notifcations when certain conditions are triggered.\r\n\r\nRead more about configuring DataDog in our [walkthrough](./docs/datadog.md).\r\n\r\n### **MySQL and Metabase**\r\nMySQL allows for reporting and querying via SQL. It can be combined with Metabase for easy exploration and visualization of the data.\r\n\r\nRead more about using these tools in our [walkthrough](./docs/SQL.md).\r\n\r\n## **Additional Topics**\r\n* [Working With CloudWatch](./docs/cloudwatch.md)\r\n* Working With Circle CI - TBC\r\n* Working With PagerDuty - TBC\r\n","readmeFilename":"README.md","gitHead":"d78b4e940c5553043b0fcc695cc0a234e2cc8e5a","_id":"bespoken-batch-tester@0.10.5","_nodeVersion":"16.5.0","_npmVersion":"7.19.1","dist":{"integrity":"sha512-AMV7JnuO9qdhOYZBiXZQOpEPaqypad3939P9cJ0me1lLI7rZAQxKf56rE0pgcgXCcQr8JxlxNC9L2YI4hOzlvw==","shasum":"7c868b14c30c7dd48028714802913ebce63714a0","tarball":"https://registry.npmjs.org/bespoken-batch-tester/-/bespoken-batch-tester-0.10.5.tgz","fileCount":30,"unpackedSize":112755,"npm-signature":"-----BEGIN PGP SIGNATURE-----\r\nVersion: OpenPGP.js v3.0.13\r\nComment: https://openpgpjs.org\r\n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\noH9+\r\n=ZRuD\r\n-----END PGP SIGNATURE-----\r\n","signatures":[{"keyid":"SHA256:jl3bwswu80PjjokCgh0o2w5c2U4LhQAE57gj9cz1kzA","sig":"MEUCIDpuSQa4H/FiHm0HwdCEHNRIbiZ36ed9f/wjdh82a7rmAiEAjCZCFdGiQeO9doddvsO8smETnQOkhmngYJGLy8pidjI="}]},"_npmUser":{"name":"jpkbst","email":"jpk@bespoken.io"},"directories":{},"maintainers":[{"name":"moisesnandres","email":"moisesnandres@hey.com"},{"name":"jpkbst","email":"jpk@bespoken.io"},{"name":"eicruzado","email":"edgarivan.cruzado@gmail.com"},{"name":"diegobst","email":"diego@bespoken.io"}],"_npmOperationalInternal":{"host":"s3://npm-registry-packages","tmp":"tmp/bespoken-batch-tester_0.10.5_1629908892524_0.11674338122868666"},"_hasShrinkwrap":false},"0.10.6":{"author":{"name":"Bespoken"},"bin":{"bbt":"bin/bbt.js"},"description":"This project tests utterances in batch - supporting a variety of data sources and reporting formats","dependencies":{"aws-sdk":"^2.718.0","axios":"^0.19.0","bespoken-tools":"2.4.45-0","commander":"^5.1.0","csv":"^5.3.2","datadog-metrics":"^0.8.1","dotenv":"^8.2.0","fuse.js":"^6.4.1","jsonpath":"^1.0.2","lodash":"^4.17.19","moment":"^2.27.0","mysql":"^2.18.1","node-cache":"^5.1.2","pino":"~6.3.2","pino-multi-stream":"^5.1.1","pino-pretty":"^4.1.0","sqlite3":"^5.0.0","virtual-device-sdk":"1.7.2-0"},"devDependencies":{"codecov":"^3.7.2","jest":"^24.9.0","jest-junit-reporter":"^1.1.0","jsdoc":"^3.6.5","standard":"^14.3.4"},"engines":{"node":">=10.0.0"},"homepage":"https://gitlab.com/bespoken/batch-tester.git","license":"ISC","jest":{"collectCoverage":true,"collectCoverageFrom":["**/src/*.js"],"testResultsProcessor":"./node_modules/jest-junit-reporter"},"main":"index.js","name":"bespoken-batch-tester","scripts":{"codecov":"codecov","docs":"jsdoc -c .jsdoc.json","postversion":"git push && git push --tags","process":"node bin/bbt.js process","standard":"standard --fix","test":"jest"},"standard":{},"version":"0.10.6","readme":"# **Bespoken Batch Tester**\r\n[Installation | Execution | GitLab | DataDog | API Docs
]() \r\nThis project enables batch testing of utterances for voice experiences.\r\n\r\nIt leverages Bespoken's Virtual Devices to run large sets of utterances through Alexa, Google Assistant, and other voice platforms.\r\n\r\n## **Getting Started**\r\n### **Installation**\r\n**This package requires Node.js 10 or greater.**\r\n\r\nTo install the Bespoken Batch Tester, just run: \r\n```\r\nnpm install bespoken-batch-tester --save\r\n```\r\n\r\nWe recommend creating a new project to store artifacts related to the tester, such as the testing configuration file, CI configuration, and custom source code.\r\n\r\n### **Environment Management**\r\nWe use dotenv when running locally, which takes environment variables from a local `.env` file.\r\n\r\nTo set this up, just make a copy of `example.env` and name it `.env`. Replace the values inside there with the correct values for your configuration.\r\n\r\nFor running with continuous integration (such as Jenkins, Circle CI or Gitlab), these values should instead be set as actual values within the CI environment.\r\n\r\n### **Virtual Device Setup**\r\n* Create a virtual device with our [easy-to-follow guide here](https://read.bespoken.io/end-to-end/setup/#creating-a-virtual-device).\r\n* Add them the configuration file, as described below\r\n\r\nIf you want to use multiple tokens, potentially for different purposes, leverage tags:\r\n```json\r\n{\r\n \"virtualDevices\": {\r\n \"myToken\": {\r\n \"tags\": [\"USAccount\"]\r\n },\r\n \"myOtherToken\": {\r\n \"tags\": [\"UKAccount\"],\r\n \"settings\": {\r\n \"listener.maxDuration\": 5\r\n }\r\n }\r\n }\r\n}\r\n```\r\n\r\nThe tags can then be assigned to a record with [record.addDeviceTag](https://bespoken.gitlab.io/batch-tester/Record.html#addDeviceTag):\r\n```\r\nrecord.addDeviceTag('USAccount')\r\n```\r\nOnly tokens that have that tag (or tags) will be used to process it.\r\n\r\nAdditionally, per device settings can be set via the `settings` property. This allows for overriding the behavior of the device as needed.\r\n\r\nFor more information on the best practices for virtual device management, [read our guide here]([docs/ACCOUNT_SETUP.md]).\r\n\r\n### **Create a Configuration File**\r\nHere is a bare minimum configuration file:\r\n```json\r\n{\r\n \"job\": \"utterance-tester\",\r\n \"sequence\": [\"open my audio player\"],\r\n \"source\": \"csv-source\",\r\n \"sourceFile\": \"path/to/my/file.csv\",\r\n \"virtualDevices\": {\r\n \"myVirtualDevice\": { \"tags\": [\"my-optional-tags\"]}\r\n }\r\n}\r\n```\r\n\r\nTo get started, cut and paste those settings into a new file, such as `batch-test.json`.\r\n\r\nMore information on configuring the batch test is below.\r\n\r\n### **Source Data**\r\nSource data by default comes from a CSV file (by default, we look at `input/records.csv`).\r\n\r\nThe structure of the file is:\r\n\r\n| Field | Required | Description|\r\n| --- | --- | --- |\r\n| utterance | Yes | The utterance to be said to the device\r\n| device | No | The device value corresponds to the tag set on the device. If this value matches a tag, that device will be eligible to process this utterance.\r\n| locale | No | The locale to use for this record. If not set, defaults to 'en-US'\r\n| voiceID | No | The voice ID to use for this record. If not set, defaults to 'en-US-Wavenet-D'\r\n| [expected values] | No | There can zero-to-many expected fields defined - these will automatically be compared to fields on the JSON output\r\n\r\n#### *device* column\r\nWith regard to the `device` column, if we have two devices, like so:\r\n```\r\n \"virtualDevices\": {\r\n \"device1\": { \"tags\": [\"google\"]},\r\n \"device2\": { \"tags\": [\"alexa\"]}\r\n }\r\n```\r\n\r\nIf our record looks like this:\r\n```csv\r\nutterance,device,transcript\r\nplay despacito,alexa,playing the song you requested\r\n```\r\n\r\nThe record will only be run with the device2, as it has the tag `alexa` that corresponds to our DEVICE value.\r\n\r\n#### *expected value* columns\r\nThe expected field values will automatically be compared to the actual value on the response from the device.\r\n\r\nFor example, if we have a record like this:\r\n```csv\r\nutterance,transcript\r\nplay despacito,playing the song you requested\r\n```\r\n\r\nOur response from the virtual device may look like this:\r\n```\r\n{\r\n \"transcript\": \"I don't know that one\"\r\n}\r\n```\r\n\r\nThe actual transcript value will be compared to the expected one, and the test will be marked a failure or success based on a partial match comparison (i.e., the actual value must include the expected value though they do not need to be an exact match).\r\n\r\nMore complex field expressions can be handled with the `fields` configuration property, [described below](#fields).\r\n\r\n### **Running the Tester**\r\nOnce the configuration file is created, just enter:\r\n```\r\nbbt process batch-test.json\r\n```\r\n\r\nAnd it will be off and running. In practice, we recommend this not be run locally but in a CI environment.\r\n\r\nThe tester will create a results.csv file, as well as publish metrics to the configured metrics provider.\r\n\r\n## **In-Depth Configuration**\r\nThe environment variables store sensitive credentials.\r\n\r\nOur configuration file stores information particular to how the tests should run, but of a non-sensitive nature.\r\n\r\nAn example file:\r\n```json\r\n{\r\n \"fields\": {\r\n \"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"\r\n },\r\n \"interceptor\": \"./src/my-interceptor\",\r\n \"job\": \"utterance-tester\",\r\n \"saveInterval\": 300,\r\n \"limit\": 5,\r\n \"maxAttempts\": 3,\r\n \"backoffTime\": 10,\r\n \"metrics\": \"datadog-metrics\",\r\n \"sequence\": [\"open my audio player\"],\r\n \"sequential\": false,\r\n \"source\": \"csv-source\",\r\n \"sourceFile\": \"path/to/my/file.csv\",\r\n \"transcript\": true,\r\n \"virtualDevices\": {\r\n \"VIRTUAL_DEVICE_1\": {\r\n \"tags\": [\"tag1\", \"tag2\"]\r\n }\r\n },\r\n \"virtualDeviceConfig\": {\r\n \"phoneNumber\": \"+1 XXX XXX XXX\",\r\n \"twilio_speech_timeout\": 2,\r\n \"twilio_timeout\": 10,\r\n \"waitTimeInterval\": 2000,\r\n \"maxWaitTime\": 300000 \r\n }\r\n}\r\n```\r\n\r\nEach of the properties is explained below:\r\n### **`fields`**\r\nEach field represents a value in the JSON response. It will be added to the result output.\r\n\r\nIf the field also is a column in the CSV file, then the value in the CSV is compared to the value in the actual response. \r\n\r\nThe fields can be mapped to complex JSON path values, such as: \r\n`\"imageURL\": \"$.raw.messageBody.directives[1].payload.content.art.sources[0].url\"`\r\n\r\nThe JSON path will be applied to the actual result from the response. If more than one value matches the JSON path expression, \r\nthen if ANY of the actual values matches the expected the test will pass.\r\n\r\n### **`interceptor`**\r\nThe interceptor allows for the core behavior of the batch runner to be modified.\r\n\r\nThere are six main methods:\r\n* interceptRecord - Called before the record is processed\r\n* interceptResult - Called before the result is finalized\r\n* interceptError - Called when the process has an error after the max attempts\r\n* interceptPreProcess - Called after the records have been loaded but before any of them is processed\r\n* interceptPostProcess - Called after all the records have been executed\r\n* interceptRequest - Called before the request is sent to a virtual device\r\n\r\nUsing [interceptRecord](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptRecord), changes can be made to the utterance or the meta data of a record before it is used in a test.\r\n\r\nUsing [interceptResult](https://bespoken.gitlab.io/batch-tester/Interceptor.html#interceptResult), changes can be made to the result of processing. This can involve:\r\n* Adding tags to the result (for use in metrics displays)\r\n* Changing the `success` flag based on custom validation logic\r\n* Adding output fields to the CSV output to provide additional information to report readers\r\n\r\nUsing `interceptError` custom code can be called after the max attempts were executed.\r\n\r\nUsing `interceptPreProcess` custom code can be called before starting the execution of the records. This can involve:\r\n* Setting up a local storage\r\n* Calling an API\r\nBe aware that this function will be called when resuming too.\r\n\r\nUsing `interceptPostProcess` custom code can be called after the execution of the records. This can involve:\r\n* Calling an API\r\n* Saving objects to a local storage.\r\n\r\nUsing `interceptRequest` the request can be modified before calling the virtual device. This can involve:\r\n* Adding more messages (before or after the main one)\r\n* Adding phrases for better speech recognition\r\n* Doing operations related to the virtual device about to be used\r\n\r\nYou can read all about the Interceptor class here:\r\nhttps://bespoken.gitlab.io/batch-tester/Interceptor.html\r\n\r\n### **`saveInterval`**\r\nTime interval where the batch job is saved. It's represented in seconds.\r\n\r\nThis defaults to 300.\r\n\r\n### **`limit`**\r\nThe numbers of records to test during test execution. Very useful when you want to try just a small subset of utterances.\r\n\r\n### **`maxAttempts`**\r\nThe number of attempts to try if a request has errors. Defaults to 3.\r\n\r\n### **`backoffTime`**\r\nThe time in seconds to wait before trying again. Defaults to 10.\r\n\r\n### **`metrics`**\r\nWe have builtin two classes for metrics: `datadog-metrics` and `cloudwatch-metrics`.\r\n\r\nThis dictates where metrics on the results of the tests are sent.\r\n\r\nAdditionally, new metric providers can be used by implementing this base class: \r\nhttps://bespoken.gitlab.io/batch-tester/Metrics.html\r\n\r\n### **`sequence`**\r\nFor tests in which there are multiple steps required before we do the \"official\" utterance that is being tested, we can specify them here.\r\n\r\nTypically, this would involve launching a skill before saying the specific utterance we want to test, but more complex sequences are possible.\r\n\r\n### **`sequential`**\r\nSetting this to true forces records to be processed one after another and not in parallel, regardless of the number of virtual devices that are configured.\r\n\r\nThis defaults to false.\r\n\r\n### **`source`**\r\nThe source for records. Defaults to `csv-source`. Additional builtin option is `s3-source`.\r\n\r\nFor the `csv-source`, the source file defaults to `input/records.csv`. This can be overridden by setting the `sourceFile` property: \r\n```\r\n{ \r\n \"sourceFile\r\n}\r\n```\r\n\r\nFor the `s3-source`, a sourceBucket must be set. Additionally, AWS credentials must be set in the environment that can access this bucket.\r\n\r\nTo implement your own custom source, [read the API docs](https://bespoken.gitlab.io/batch-tester/Source.html).\r\n\r\n### **`transcript`**\r\nIf set to false, speech-to-text is not performed on the audio response from the device\r\n\r\n### **`virtualDevices`**\r\nSee the section above for information on [configuring virtual devices](#virtual-device-setup).\r\n\r\n### **`virtualDeviceBaseURL`**\r\nFor values other than the default (`https://virtual-device.bespoken.io`), set this property.\r\n\r\n### **`virtualDeviceConfig`**\r\nAllows setting properties to all virtual devices. For example, when using Twilio Virtual Devices, setting the `phone_number` to call.\r\n\r\n## Advanced Execution\r\n### Resuming A Job\r\nTo resume a job that did not complete, due to errors or timeout, simply set the `RUN_KEY` environment variable.\r\n\r\nThe run key can be found in the logs for any run - it will appear like this: \r\n```\r\nBATCH SAVE completed key: 7f6113df3e2af093f095d2d3b2505770d9af1c057b93d0dff378d83c0434ec61\r\n```\r\n\r\nThe environment variable can be set locally with:\r\n```\r\nexport RUN_KEY=\r\n\r\nTo set a log level, simply set the following env variable: `export LOG_LEVEL=debug`. If not set, `info` is assumed.\r\n\r\nWe can also colorize the outputs by setting the env variable `COLORIZE`.\r\n\r\n\r\n\r\nTimestamps can be added to the outputs by setting the env variable `DISPLAY_LOG_TIME`.\r\n\r\n\r\n\r\nFinally, logs can be saved to a file by setting the env variable `SAVE_LOG_FILE`. The file will be placed in the `output` folder and named `batch-tester.log`. It uses pino.js default format.\r\n\r\n## Type of Utterances\r\nThe record class accepts three types of utterances: text, URL and local audio files.\r\n\r\n| Device | Support |\r\n| --- | --- |\r\n| Alexa | Text, URL, Local Audio File |\r\n| Google Assistant | Text, URL, Local Audio File |\r\n| Test Robot | Text, URL, Local Audio File |\r\n| IVR | Text, URL |\r\n\r\n### Text\r\n```javascript\r\n const record = new Record('play a radio station')\r\n```\r\n\r\n### URL\r\n```javascript\r\n const record = new Record('https://play.radio.com/1234.wav')\r\n```\r\n\r\n### Local audio file\r\nThe audio file path should start from the root path of your project\r\n```\r\nproject/\r\n|-subfolder/\r\n |- audios/\r\n |- radio123.wav\r\n|- src/\r\n |- source.js\r\n```\r\n```javascript\r\n const record = new Record('./subfolder/audio/radio123.wav')\r\n```\r\n\r\n## **DataDog Configuration**\r\nFollow [this guide](./docs/datadog#how-to-signup-and-get-an-api-key) to get started.\r\n\r\n## **Gitlab Configuration**\r\nThe gitlab configuration is defined by the file `.gitlab-ci.yml`. The file looks like this:\r\n```yaml\r\nimage: node:10\r\n\r\ncache:\r\n paths:\r\n - node_modules/\r\n\r\nutterance-tests:\r\n script:\r\n - npm install\r\n - npm test\r\n artifacts:\r\n when: always\r\n paths:\r\n - test_output/report/index.inline.html\r\n - test-report.xml\r\n reports:\r\n junit: test-report.xml\r\n only: \r\n - schedules\r\n - web\r\n```\r\n\r\nWhen the GitLab Runner is executed, it takes this file and creates a Linux instance with Node.js, executes the commands under the `script` element, and saves the reports as artifacts.\r\n\r\n#### **Setting a schedule**\r\nIt is very easy to run your end-to-end tests regularly using GitLab. Once your CI file (`.gitlab-ci.yml`) has been uploaded to the repository just go to \"CI/CD => Schedules\" from the left menu. Creating a new schedule looks like this:\r\n\r\n[](docs/images/GitLabCISchedule-1.png)\r\n\r\n[](docs/images/GitLabCISchedule-2.png)\r\n\r\n## **Test Reporting**\r\nWe have setup this project to make use of a few different types of reporting to show off what is possible.\r\n\r\nThe reporting comes in these forms:\r\n* CSV File that summarizes results of utterance tests\r\n* Reporting via DataDog\r\n* Reporting via SQLite and Metabase\r\n\r\nEach is discussed in more detail below.\r\n\r\n### **CSV File**\r\nThe CSV File contains the following output:\r\n\r\n| Column | Description |\r\n| --- | --- |\r\n| name | The name of the receipt to ask for\r\n| transcript | The actual response back from Alexa\r\n| success | Whether or not the test was successful\r\n| expectedResponses | The possible expected response back from the utterance\r\n\r\n### **DataDog**\r\nDataDog captures metrics related to how all the tests have performed. Each time we run the tests, and when `datadog` has been set as the `metric` mechanism to use in the `config.json` file, we push the result of each test to DataDog.\r\n\r\nIn general, we are using next metrics:\r\n- `utterance.success`\r\n- `utterance.failure`\r\n\r\nThe metrics can be easily reported on through a DataDog Dashboard. They also can be used to setup notifcations when certain conditions are triggered.\r\n\r\nRead more about configuring DataDog in our [walkthrough](./docs/datadog.md).\r\n\r\n### **MySQL and Metabase**\r\nMySQL allows for reporting and querying via SQL. It can be combined with Metabase for easy exploration and visualization of the data.\r\n\r\nRead more about using these tools in our [walkthrough](./docs/SQL.md).\r\n\r\n## **Additional Topics**\r\n* [Working With CloudWatch](./docs/cloudwatch.md)\r\n* Working With Circle CI - TBC\r\n* Working With PagerDuty - TBC\r\n","readmeFilename":"README.md","gitHead":"8dda18c9d22377188468ea53c7c696e30f362ef6","_id":"bespoken-batch-tester@0.10.6","_nodeVersion":"16.5.0","_npmVersion":"7.19.1","dist":{"integrity":"sha512-mgcvG25Hu3zW/0AUT5xRg5KoDJYmPwayZeGJ4CLTE6hMNIzByerZuO3VDaISgTWoSZwJMH6ts3V6151ddvCUzw==","shasum":"e6abc66c8566040e1f174ea50f07fea59592748b","tarball":"https://registry.npmjs.org/bespoken-batch-tester/-/bespoken-batch-tester-0.10.6.tgz","fileCount":30,"unpackedSize":112755,"npm-signature":"-----BEGIN PGP SIGNATURE-----\r\nVersion: OpenPGP.js v3.0.13\r\nComment: 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