The A9 Jira Data Generation Cookbook or "It's 2023, do you know where your example Jira data is?"
If you've use the Jira data generator on server or data center, you know it has powerful metadata generation capabilities. In addition, it can create users and groups. It's pretty great if you want to synthesize a long-lived and complex Jira instance with changelogs, comments, etc.
In the cloud, it's a different story; the add-ons for data generation that I've found in the marketplace don't seem to extend as far. It's difficult to find a path to generate everything that the old one did, so I went on a mission.
That mission was to utilize tool assisted speed development to the fullest. I subscribe to ChatGPT Pro which gives me access to 3 plug-ins in a single chat session. My favorite (right now) is Noteable , which not only gives me a shareable python notebook, but by using the plug-in, I have a Python "developer" with debugging capabilities in my browser. This is game changing for me. I can think in terms of the features I want to see. I can provide example data and using the web browsing plug-in I can send chat, GPT on a hunt I can give it in a swagger file or REST API docs and receive debugged python code in another browser tab. Fantastic stuff.
What to do with it?
Prompt it to improve your life or in my case, development conditions. I need some robust and complex Jira data to explore and develop my algorithm for assignment accuracy. Teams in Space would be my go-to for this, since the context in the issues is actually somewhat representative of a real-world environment. Approximating this is the goal; issue types, personas, some commonality between issuetypes, etc. Not the banal Lorem Ipsum we sometimes get. Sure, you or I could run my NLP notebooks on the Issues, but it's a case of garbage in, garbage out.
Enter the OpenAI API
In contrast to the static data in Teams in Space or the gobbledygook some of the other tools generate, I want contextual and relevant Jira Summaries and Descriptions. Using the OpenAI API, I can prompt gpt-3.5-turbo to spit out some believable synthetic bugs. I can set a subject, specify the issuetype, quantity and a few other parameters.
In this example, we're looking at bugs from an Auction Site, and the bugs are related to Project 'Greenspoon' (must be one of those inscrutable product code names, which you can also specify)
Example
Here's an example Bug:
Summary: Unable to add products to the cart on Greenspoon website
Description:
Steps to Reproduce:
1. Go to the Greenspoon website.
2. Search for a product using the search bar.
3. Select a product from the search results.
4. Click on the "Add to Cart" button.
Expected Result:
The selected product should be added to the cart and a success message should be displayed.
Actual Result:
After clicking on the "Add to Cart" button, the product is not added to the cart, and no success message is shown. The cart remains empty.
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Additional Information:
- This issue is reproducible on different browsers (Chrome, Firefox, and Safari) and on both desktop and mobile devices.
- Clearing browser cache and cookies did not resolve the problem.
- Other website features (e.g., search functionality, account login) are working as expected.
Impact:
This bug prevents users from adding products to their carts, resulting in a loss of potential sales and frustration among customers.
Priority: Medium
Severity: High
We can also do Epics, Tasks and Subtasks.
Example Epic:
Epic Description: Project Greenhorn aims to improve the user onboarding experience and increase user engagement on our high-tech auction site. By focusing on enhancing the initial user journey and providing users with a seamless and engaging onboarding process, we can boost user satisfaction and retention rates. Key Objectives: 1. Streamline Registration: Implement a frictionless registration process, eliminating unnecessary steps and reducing user drop-offs during account creation. 2. Personalized Recommendations: Develop algorithms and mechanisms to provide personalized item recommendations to new users based on their preferences and browsing behavior. 3. Interactive Onboarding: Create an interactive onboarding tutorial using innovative techniques such as interactive walkthroughs, tooltips, and gamification to guide users through platform features and best practices. 4. Social Integration: Integrate social media platforms to allow users to easily connect their accounts, share their auction activities, and invite friends, fostering a sense of community and encouraging user engagement. 5. Education Center: Build an extensive knowledge base and education center to provide users with comprehensive resources, tutorials, and FAQs, helping them become proficient users and reducing support requests. 6. Notifications and Reminders: Implement a notification system to proactively engage users, providing updates on bidding activity, winning auctions, or outbid notifications, encouraging them to stay active and participate in auctions. 7. Feedback Mechanism: Establish a feedback mechanism to collect user suggestions, comments, and complaints, enabling continuous improvement of our platform and addressing user pain points. By tackling these objectives as part of the Project Greenhorn epic, we expect to attract and retain more users, drive higher engagement levels, and ultimately increase the success and growth of our high-tech auction site.
These synthetic data are saved as CSV files in Noteable, which you can download and edit to suit.
The notebook also has provisions (in progress) to allow upload directly from the notebook.
Guide for Jira Setup
Before we can import the issues into Jira, we need to set up the Jira credentials. Follow these steps:
You will use this token to authenticate with the Jira API.
In the final cell of the Data Generator Cookbook, you can find the basic python code to connect to a Cloud Jira instance.
I hope this helps you generate relevant Jira example data!
More:
Tool Assisted Speed Development Podcast Episode (a real snoozefest, if I say so myself - listen @ 2X): https://open.spotify.com/episode/2osAkOmy573gnPvvWRfyM6?si=jrZlG-juRZmWYpaJLv0xNQ&nd=1
A9 Jira Data Generator Cookbook: https://app.noteable.io/published/237ca962-080b-43f0-b69d-8b14ae99f452