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v0.20.x
  • Introduction to AI Testing
  • Welcome to Distributional
  • Motivation
  • What is AI Testing?
  • Stages in the AI Software Development Lifecycle
    • Components of AI Testing
  • Distributional Testing
  • Getting Access to Distributional
  • Learning about Distributional
    • The Distributional Framework
    • Defining Tests in Distributional
      • Automated Production test creation & execution
      • Knowledge-based test creation
      • Comprehensive testing with Distributional
    • Reviewing Test Sessions and Runs in Distributional
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      • Insights surfaced elsewhere on Distributional
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    • Data in Distributional
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  • Using Distributional
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      • Eval Module
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          • Index of functions
          • eval
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    • Notifications
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    • Hello World (Sentiment Classifier)
    • Trading Strategy
    • LLM Text Summarization
      • Setting the Scene
      • Prompt Engineering
      • Integration testing for text summarization
      • Practical considerations
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  1. Using Distributional
  2. Testing
  3. Production Testing

Notable Results

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After a Test Session is executed, users might want to introspect on which Tests passed and failed. In addition, they want to understand what caused a Test or a group of Tests to fail; in particular, which subset of from the Run likely caused the Tests to fail.

Notable results are only presented for Distributional

Reviewing notable results

To review the notable results, first select the desirable set of Generated Tests that you want to study from the Test Session details page. This may include both failed and passed Tests.

Click VIEW TEST ANALYSIS button to enter the Test Analysis page. On this page, you can review the notable results for the Experiment Run and Baseline Run under the Notable Results tab.

results
generated tests