Blog GenAI This week’s AI Bite: How to generate test cases based on Jira tickets – A guide by Sylwia RapaczThis week’s AI Bite: How to generate test cases based on Jira tickets – A guide by Sylwia Rapacz
Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work. Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects.What models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? Check out Boldare’s channels every Monday for the latest weekly AI bite.In the latest post, Sylwia Rapacz, a QA Engineer at Boldare, shares her experiences and tips on automating the generation of test cases (TCs) based on Jira tickets. With the help of Claude Code, Sylwia demonstrates how to use artificial intelligence to quickly and accurately create test scenarios, significantly improving the efficiency of QA teams.
Table of contents
A step toward the future in QA
Generating test cases based on Jira tickets is a process that can take up a lot of time and is naturally prone to human errors. Traditionally, this involves analyzing acceptance criteria, planning, and transforming that information into appropriate test scenarios, which are then manually written and entered into a selected tool. Claude Code simplifies this process by automating it through Python scripts and the Jira API. The tool directly connects to Jira, retrieves ticket details such as acceptance criteria, and generates a .csv file with test scenarios, including both ideal paths and error or edge cases.
Claude Code generates tests in .csv format, which can be easily imported into test management tools (e.g., Testmo). Automating this process makes the creation of test scenarios 96% faster, reduces the errors arising from manual creation, and provides better coverage.
Benefits of automating test cases – Time saving and greater consistency
Automating the generation of test cases using Claude Code brings several benefits:
- Faster TC creation: Instead of hours spent writing test cases, the entire process now takes just a few minutes. Of course, time is still required to analyze the generated scenarios and make any necessary adjustments.
- Better coverage of negative scenarios: AI focuses on edge tests and errors that might be overlooked in a manual process. In particular, Claude Code highlights issues like missing data, form errors, or API problems, which are crucial in detecting rare but serious bugs.
- More consistency in formats: Tests are created according to a unified template, eliminating discrepancies between team members.
Thanks to this, the QA team can focus on more strategic tasks, such as requirement analysis, test results interpretation, and solving more complex problems, rather than spending time creating test cases.
Claude Code in action – A practical example
Sylwia presents real-world examples of using Claude Code to generate test cases. For instance, based on a ticket about employee transfer, AI generated over 10 test scenarios in just 2 minutes, which in traditional conditions might have taken 1-2 hours.
Summary
Automating the generation of test cases with Claude Code can significantly improve the efficiency of QA teams. Instead of spending valuable hours manually writing tests, teams can now focus on more advanced tasks, while Claude Code takes care of creating test scenarios quickly and accurately. It’s a practical tool that brings real change to the QA process, increasing test efficiency, accuracy, and consistency.
It’s important to remember that the starting point is a good description of requirements, and it’s always advisable to review, analyze, and make any necessary adjustments to the AI-generated scenarios.
If you want to learn more about how Claude Code can support your QA team, follow our blog for more innovative solutions!
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