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Regression AI-Infused Application Testing

Quickly learn about Regression AI-Infused Application Testing specifics

Zorica Micanovic avatar
Written by Zorica Micanovic
Updated over 4 months ago

Regression AI-Infused Application Testing is a systematic approach that allows you to ensure the consistent performance of the AI. This method involves testing the AI using prepared prompts in the form of Test Cases, designed to verify that previously developed and tested software still performs correctly after changes.

Key Features of Regression AI-Infused Application Testing

  1. Systematic and Structured: Regression AI-Infused Application Testing follows a structured approach in testing, using prepared prompts. This ensures that all aspects of the AI are thoroughly tested and that nothing is overlooked.

  2. Ensuring Consistency: The primary goal of Regression AI-Infused Application Testing is to ensure that changes to the AI, such as bug fixes or new features, have not adversely affected existing functionality.

The Regression Testing Flow

It consists of:

  • Testing AI using prompts provided in the form of Test Cases

  • Evaluating the responses

  • Submitting Reports

Regression AI-Infused Application Tests Breakdown

  • Each Regression Test Cycle will offer you a set of prompts to be tested.

  • Once you reserve the seat for Test Case Execution, execute and document steps as in any other Test Case Test Cycle.

  • If you encounter a bug, fail the step and file the bug.

The attachments for each step should be screenshots unless a screencast is needed to understand the issue.

Regression AI-Infused Application Testing allows you to ensure the consistent performance of the LLM, even as changes are made. Remember, the goal of Regression AI-Infused Application Testing is not just to find bugs, but also to ensure that the AI continues to perform as expected, providing reliable and accurate results. When the test scenario fails, it offers valuable insights that can be used to enhance the AI's performance and reliability.

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