Release Notes: Custom Fields in JIRA, Generative AI Test Case Generation, Validate Outgoing Browser API Calls, CAPTCHA

Release Notes: Custom Fields in JIRA, Generative AI Test Case Generation, Validate Outgoing Browser API Calls, CAPTCHA.


This week, we added support for custom fields with our Jira integration, we released our first iteration of Generative AI for test case generation, we added browser API validation, and Captcha.

JIRA (Custom Fields)

testRigor now allows users to assign values for custom fields in Jira. The name of the Jira field must be the one that starts with customfield_.

Test Case Generation (Generative AI)

testRigor released Generative AI capability to allow our customers to generate tests based on just test case description.

See detailed documentation about this functionality here.


testRigor has added a way to validate API calls made by the browser filtering by request URL, request headers and request body. The validation also allows users to specify the response status code and the response headers. This is only supported on Chrome and Edge browsers.

See more documentation on this feature here.


testRigor has added support to resolve image to text and google re-captcha type captchas.
This command requires the user to contact sales to activate it on their plan, as it is an extra paid feature.

For image type captchas we need the user to specify the element

resolve captcha of type image from "normal captcha"

For Google recaptcha type captchas we will detect it from the page

resolve captcha of type recaptcha

See detailed documentation here.


AI Applications

The Ultimate Testing Terms Glossary can be leveraged for various AI applications in the field of software testing and quality assurance. Here are some potential AI applications:

1. **Automated Testing**: The glossary can be used to train natural language processing (NLP) models for automated test case generation and test script development. By understanding the definitions and nuances of testing terms, AI can assist in creating comprehensive test cases.

2. **Intelligent Test Automation**: AI can utilize the glossary to develop intelligent test automation frameworks that understand and adapt to the specific testing terminology used in different projects and industries, improving the accuracy and effectiveness of automated testing processes.

3. **Natural Language Understanding for Testing**: The glossary can serve as a knowledge base for training AI models to understand and interpret natural language queries and commands related to testing, enabling AI-powered testing assistants and chatbots.

4. **Quality Assurance Analysis**: AI can analyze the usage and context of testing terms in software documentation, requirements, and defect reports to provide insights into the quality assurance process, identify potential ambiguities, and suggest improvements.

5. **Predictive Analytics for Testing**: By incorporating the glossary into AI models, predictive analytics can be applied to testing data to anticipate potential testing challenges, optimize testing strategies, and forecast defect patterns based on the understanding of testing terms and concepts.

6. **Continuous Improvement of Testing Practices**: AI can use the glossary to continuously learn and adapt to evolving testing terminology and best practices, enabling the development of AI-driven tools for quality assurance that stay up-to-date with industry standards.

In essence, the Ultimate Testing Terms Glossary can act as a foundational resource for training AI models and developing intelligent tools that enhance various aspects of software testing and quality assurance processes.