Streamline Your Development Workflow: Integrating Ginkgo with Jira, Elasticsearch, Webex, and Slack
Tags: kubernetes, showdev, testing, go
Published: 2023-04-12 07:08:52

Are you tired of juggling multiple tools and platforms to manage your development workflow? Ginkgo, a powerful testing framework, can help simplify your process and streamline your team's efforts. And by integrating it with Jira, Elasticsearch, Webex, and Slack, you can create a seamless workflow that ensures everyone stays on the same page.

In this article, we'll explore how to set up these integrations and leverage the power of Ginkgo to improve your team's productivity. Whether you're a seasoned developer or just starting out, these tips will help you take your workflow to the next level.

**Step 1: Integrate Ginkgo with Jira**
Jira is a popular project management tool that can help you keep track of your development tasks and issues. By integrating Ginkgo with Jira, you can easily track your test cases and results, and ensure that everyone on your team is aware of any issues that arise.

**Step 2: Use Elasticsearch for Better Searchability**
Elasticsearch is a powerful search and analytics engine that can help you easily search through your test results and identify any patterns or trends. By integrating Ginkgo with Elasticsearch, you can take advantage of its powerful features and make your workflow even more efficient.

**Step 3: Leverage Webex for Collaboration**
Webex is a popular video conferencing tool that can help you stay connected with your team and collaborate more effectively. By integrating Ginkgo with Webex, you can easily schedule meetings and share test results, ensuring that everyone is on the same page.

**Step 4: Keep Everyone in the Loop with Slack**
Slack is a popular team communication tool that can help you stay connected with your team and keep everyone informed of any updates or changes. By integrating Ginkgo with Slack, you can easily share test results and notifications, ensuring that everyone is always up to date.

By following these steps and integrating Ginkgo with Jira, Elasticsearch, Webex, and Slack, you can create a streamlined and efficient workflow that improves your team's productivity and ensures that everyone is on the same page. So why not give it a try?

**Getting started**

All that is needed is:
- importing this [library](https://github.com/gianlucam76/ginkgo-tracker-notifier)
- call it from test suite passing correct information.

```go
ginkgo_helper.Register(context.TODO(),
  ginkgo_helper.WithRunID(12346),
  ginkgo_helper.WithWebex(webexInfo),
  ginkgo_helper.WithElastic(elasticInfo),
  ginkgo_helper.WithJira(jiraInfo),
  )
```

where elasticInfo, webexInfo, jiraInfo contains necessary information to access Webex, Elastic DB, Jira Board.

[README](https://github.com/gianlucam76/ginkgo-tracker-notifier#readme) contains more information.

The idea is simple: whenever a test fails, Ginkgo can automatically file a Jira issue and add it to the current sprint. Most of the necessary information is already available, but there's one key piece missing: the assignee. That's where Ginkgo Labels come in.

By adding a Label like _maintainer:<user>_ to a test, you can specify who should be assigned the corresponding Jira issue. This way, you can ensure that each issue is assigned to the right team member, saving time and reducing the risk of miscommunication.

To set up this feature, simply add the appropriate label to each test in your Ginkgo suite. Then, configure the Jira integration to automatically create issues and assign them based on the label. With this simple but powerful feature, you can streamline your development process and keep your team running at peak efficiency.


![Jira issue filed by https://github.com/gianlucam76/ginkgo-tracker-notifier](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/yj7cq8h8gayqbzphopm5.png)

The next steps came naturally. Why stops at simply filing a Jira issue. Let’s also send a summary for each failed test to a Webex space (or Slack channel).

Finally, store each e2e run in an elasticsearch DB. This allowed us to do any type of analysis later on like for instance:
- how many times a specific test failed;
- last run a test passed/failed;
-plot duration of a test across multiple runs.