Data Testing
Designing Tests for Edge Case Data Scenarios
This prompt helps data science teams create test cases to evaluate how datasets handle edge case scenarios. It focuses on identifying and testing extreme, rare, or unexpected data conditions to ensure robustness in downstream processes.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering, QA
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Identify the dataset’s known boundaries, rare categories, and potential anomalies.
Define processes or models that could be impacted by edge cases.
Gather tools or libraries for simulating and validating edge scenarios.
THE PROMPT
Help create test cases for identifying and testing edge case scenarios in [specific dataset, e.g., transactional data from an e-commerce platform]. Focus on:
Extreme Values: Recommending tests, such as, ‘Identify outliers and test how processes handle extreme values, like unusually high or low amounts in [specific column].’
Rare Categories: Suggesting validation, like, ‘Evaluate how models or pipelines handle underrepresented categories in [specific feature, e.g., product type or region].’
Data Anomalies: Including anomaly detection, such as, ‘Test for invalid values, like negative ages, or out-of-range timestamps in time-series data.’
Boundary Conditions: Proposing boundary checks, such as, ‘Validate how data handling processes perform near thresholds, like minimum or maximum allowable limits.’
Sparse or Missing Data: Recommending tests for gaps, such as, ‘Simulate sparse datasets or missing values to ensure processes remain functional without errors.’
Provide a comprehensive plan for testing edge case scenarios to ensure data robustness and integrity. If additional details about the dataset or intended use case are needed, ask clarifying questions to refine the test cases.
Bonus Add-On Prompts
Propose strategies for automating edge case testing using synthetic or augmented datasets.
Suggest methods for logging and reporting failures related to edge cases.
Highlight tools like Faker or Synth for generating data to test edge cases.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on testing edge cases in domain-specific datasets, like healthcare or financial data.
Include tips for combining edge case testing with regular validation pipelines.
Propose ways to document identified edge cases for future analysis and monitoring.
Highlight tools like Great Expectations or Pandera for automating edge case validations.
Add suggestions for integrating edge case results with dashboards or alerts.
WHEN TO USE
During data preprocessing or model validation to ensure robustness against rare conditions.
When designing systems for high-stakes industries where edge cases can have significant impacts.
To test workflows or pipelines for stability and resilience under unexpected inputs.
WHEN NOT TO USE
For datasets with well-defined and consistent boundaries that do not include rare conditions.
If edge case scenarios are irrelevant to the use case or application.