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Back to NLP Topics Batch Testing evaluates your app’s ability to correctly identify expected intents and entities from a set of utterances. It provides statistical analysis of ML model performance. Go to: Automation AI > Virtual Assistant > Testing > Regression Testing > Batch Testing
Batch Testing supports the Zero-shot Model for intent detection. Ensure the Zero-shot ML Model feature is enabled and the ML Network Type is set to Zero-shot model.

Best Practices

  • Build a test suite of representative utterances first, then train against failures.
  • Update test suites regularly for high-usage utterances.
  • Publish only after thorough testing.
  • Keep intent names short (3-5 words), avoid special characters and stop words.
  • Batch tests don’t consider conversation context — some False Negatives may be True Positives in live sessions.
  • The “count” in batch results refers to unique assertion statements, not CSV rows. Consecutive rows with the same utterance and different entity values count as one assertion.
  • Batch testing scores original input, not spell-corrected input.

Test Suite Types


Creating a Test Suite

  1. Go to Batch Testing and click New Test Suite.
  2. Enter a Name and Description.
  3. Choose how to add test cases:
    • Add Manually — enter cases by hand or auto-generate with LLM.
    • Upload a File — import a CSV or JSON file (max 1000 utterances).

Adding Test Cases Manually

  1. In the test suite, click +Add Test Case.
  2. Fill in:
    • Intent — the dialog task to test. You can tag up to 3 intents (Dialog, FAQ, Small Talk) per utterance for by-design ambiguity scenarios.
    • Parent Intent — for child intents.
    • Test Utterances — one per line.
    • Entity Order — extraction order (not available when multiple intents are selected).
  3. Click Save.

Auto-Generating Test Cases

Requires LLM and Generative AI to be enabled.
  1. Click Generate Test Cases.
  2. Select the Dialog Task.
  3. Click Generate and wait for results.
  4. Review suggestions; reject unwanted ones or click Generate more.
  5. Click Add Test Cases.

Test Suite File Formats

JSON Format

Multi-item entities: "entityValue": "Apples||Grapes" Composite entities: "entityValue": "City:Hyderabad|Date:2018-07-06|Curr:1200 INR" JSON properties:

CSV Format

CSV columns:

Entity Format Conversions


Running a Test Suite

  1. Click the test suite name in the Batch Testing window.
  2. Select In Development or Published.
  3. Click Run Test Suite.
New test suites automatically trigger runs for both In-Development and Published versions. Add notes: Click the Notes icon during or after a run to record the purpose or changes. Max 1024 characters.

Cancel a Running Test

  1. Click the Cancel icon next to the running suite.
  2. Confirm with Yes.
Notes:
  • Canceled tests can’t produce a downloadable CSV report.
  • Orange warning icon = canceled. Red warning icon = failed due to technical error.
  • Can’t cancel if another test execution or cancellation is in progress.
  • Can’t cancel a Published-mode run if the app hasn’t been published.

Test Results

Each run shows: Run outcomes: Success, Success with warning (some records discarded), or Failed (system error).

Elimination Reason

When expected intent ≠ winning intent, the Elimination Reason column shows why. R&R policy reasons take precedence; otherwise scores from each engine are shown (FM: [score], ML: [score], FAQ: [score]).

CSV Report Fields

Download the report using the Download icon. Summary fields: Bot Name, Report Name, Bot Language, Run Type, Threshold Settings (mode, minThreshold, maxThreshold, exactMatchThreshold, isActive, taskMatchTolerance, wordCoverage, suggestionsCount, pathCoverage), Last Tested, Utterance Count, Success/Failure Ratio, TP, TN, FP, FN. Per-utterance fields: Utterance, Expected Intent, Matched Intent, Parent Intent, Task State, Result Type, Entity Name, Expected EntityValue, Matched EntityValue, Entity Result, Expected Entity Order, Actual Entity Order, Matched Intent Score (FM/ML/KG), Expected Intent Score, Elimination Info.