PICT Test Designer

This skill enables systematic test case design using PICT (Pairwise Independent Combinatorial Testing). Given requirements or code, it analyzes the system to identify test parameters, generates a PICT model with appropriate constraints, executes the model to generate pairwise test cases, and formats the results with expected outputs.

When to Use This Skill

Use this skill when:

Designing test cases for a feature, function, or system with multiple input parameters

Creating test suites for configurations with many combinations

Needing comprehensive coverage with minimal test cases

Analyzing requirements to identify test scenarios

Working with code that has multiple conditional paths

Building test matrices for API endpoints, web forms, or system configurations

Workflow

Follow this process for test design:

1. Analyze Requirements or Code

From the user's requirements or code, identify:

Parameters

Input variables, configuration options, environmental factors

Values

Possible values for each parameter (using equivalence partitioning)

Constraints

Business rules, technical limitations, dependencies between parameters
Expected Outcomes: What should happen for different combinations Example Analysis: For a login function with requirements: Users can login with username/password Supports 2FA (on/off) Remembers login on trusted devices Rate limits after 3 failed attempts Identified parameters: Credentials: Valid, Invalid TwoFactorAuth: Enabled, Disabled RememberMe: Checked, Unchecked PreviousFailures: 0, 1, 2, 3, 4 2. Generate PICT Model Create a PICT model with: Clear parameter names Well-defined value sets (using equivalence partitioning and boundary values) Constraints for invalid combinations Comments explaining business rules Model Structure:

Parameter definitions

ParameterName: Value1, Value2, Value3

Constraints (if any)

IF [Parameter1] = "Value" THEN [Parameter2] <> "OtherValue"; Refer to references/pict_syntax.md for: Complete syntax reference Constraint grammar and operators Advanced features (sub-models, aliasing, negative testing) Command-line options Detailed constraint patterns Refer to references/examples.md for: Complete real-world examples by domain Software function testing examples Web application, API, and mobile testing examples Database and configuration testing patterns Common patterns for authentication, resource access, error handling 3. Execute PICT Model Generate the PICT model text and format it for the user. You can use Python code directly to work with the model:

Define parameters and constraints

parameters

{ "OS" : [ "Windows" , "Linux" , "MacOS" ] , "Browser" : [ "Chrome" , "Firefox" , "Safari" ] , "Memory" : [ "4GB" , "8GB" , "16GB" ] } constraints = [ 'IF [OS] = "MacOS" THEN [Browser] IN {Safari, Chrome}' , 'IF [Memory] = "4GB" THEN [OS] <> "MacOS"' ]

Generate model text

model_lines

[ ] for param_name , values in parameters . items ( ) : values_str = ", " . join ( values ) model_lines . append ( f" { param_name } : { values_str } " ) if constraints : model_lines . append ( "" ) for constraint in constraints : if not constraint . endswith ( ';' ) : constraint += ';' model_lines . append ( constraint ) model_text = "\n" . join ( model_lines ) print ( model_text ) Using the helper script (optional): The scripts/pict_helper.py script provides utilities for model generation and output formatting:

Generate model from JSON config

python scripts/pict_helper.py generate config.json

Format PICT tool output as markdown table

python scripts/pict_helper.py format output.txt

Parse PICT output to JSON

python scripts/pict_helper.py parse output.txt To generate actual test cases , the user can: Save the PICT model to a file (e.g., model.txt ) Use online PICT tools like: https://pairwise.yuuniworks.com/ https://pairwise.teremokgames.com/ Or install PICT locally (see references/pict_syntax.md) 4. Determine Expected Outputs For each generated test case, determine the expected outcome based on: Business requirements Code logic Valid/invalid combinations Create a list of expected outputs corresponding to each test case. 5. Format Complete Test Suite Provide the user with: PICT Model - The complete model with parameters and constraints Markdown Table - Test cases in table format with test numbers Expected Outputs - Expected result for each test case Output Format Present results in this structure:

PICT Model

# Parameters
Parameter1: Value1, Value2, Value3
Parameter2: ValueA, ValueB
# Constraints
IF [Parameter1] = "Value1" THEN [Parameter2] = "ValueA";

|

Test Case Summary

Total test cases: N

Coverage: Pairwise (all 2-way combinations)

Constraints applied: N Best Practices Parameter Identification Good: Use descriptive names: AuthMethod , UserRole , PaymentType Apply equivalence partitioning: FileSize: Small, Medium, Large instead of FileSize: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Include boundary values: Age: 0, 17, 18, 65, 66 Add negative values for error testing: Amount: ~-1, 0, 100, ~999999 Avoid: Generic names: Param1 , Value1 , V1 Too many values without partitioning Missing edge cases Constraint Writing Good: Document rationale:

Safari only available on MacOS

Start simple, add incrementally Test constraints work as expected Avoid: Over-constraining (eliminates too many valid combinations) Under-constraining (generates invalid test cases) Complex nested logic without clear documentation Expected Output Definition Be specific: "Login succeeds, user redirected to dashboard" "HTTP 400: Invalid credentials error" "2FA prompt displayed" Not vague: "Works" "Error" "Success" Scalability For large parameter sets: Use sub-models to group related parameters with different orders Consider separate test suites for unrelated features Start with order 2 (pairwise), increase for critical combinations Typical pairwise testing reduces test cases by 80-90% vs exhaustive Common Patterns Web Form Testing parameters = { "Name" : [ "Valid" , "Empty" , "TooLong" ] , "Email" : [ "Valid" , "Invalid" , "Empty" ] , "Password" : [ "Strong" , "Weak" , "Empty" ] , "Terms" : [ "Accepted" , "NotAccepted" ] } constraints = [ 'IF [Terms] = "NotAccepted" THEN [Name] = "Valid"' ,

Test validation even if terms not accepted

] API Endpoint Testing parameters = { "HTTPMethod" : [ "GET" , "POST" , "PUT" , "DELETE" ] , "Authentication" : [ "Valid" , "Invalid" , "Missing" ] , "ContentType" : [ "JSON" , "XML" , "FormData" ] , "PayloadSize" : [ "Empty" , "Small" , "Large" ] } constraints = [ 'IF [HTTPMethod] = "GET" THEN [PayloadSize] = "Empty"' , 'IF [Authentication] = "Missing" THEN [HTTPMethod] IN {GET, POST}' ] Configuration Testing parameters = { "Environment" : [ "Dev" , "Staging" , "Production" ] , "CacheEnabled" : [ "True" , "False" ] , "LogLevel" : [ "Debug" , "Info" , "Error" ] , "Database" : [ "SQLite" , "PostgreSQL" , "MySQL" ] } constraints = [ 'IF [Environment] = "Production" THEN [LogLevel] <> "Debug"' , 'IF [Database] = "SQLite" THEN [Environment] = "Dev"' ] Troubleshooting No Test Cases Generated Check constraints aren't over-restrictive Verify constraint syntax (must end with ; ) Ensure parameter names in constraints match definitions (use [ParameterName] ) Too Many Test Cases Verify using order 2 (pairwise) not higher order Consider breaking into sub-models Check if parameters can be separated into independent test suites Invalid Combinations in Output Add missing constraints Verify constraint logic is correct Check if you need to use NOT or <> operators Script Errors Ensure pypict is installed: pip install pypict --break-system-packages Check Python version (3.7+) Verify model syntax is valid References references/pict_syntax.md - Complete PICT syntax reference with grammar and operators references/examples.md - Comprehensive real-world examples across different domains scripts/pict_helper.py - Python utilities for model generation and output formatting PICT GitHub Repository - Official PICT documentation pypict Documentation - Python binding documentation Online PICT Tools - Web-based PICT generator Examples Example 1: Simple Function Testing User Request: "Design tests for a divide function that takes two numbers and returns the result." Analysis: Parameters: dividend (number), divisor (number) Values: Using equivalence partitioning and boundaries Numbers: negative, zero, positive, large values Constraints: Division by zero is invalid Expected outputs: Result or error PICT Model: Dividend: -10, 0, 10, 1000 Divisor: ~0, -5, 1, 5, 100 IF [Divisor] = "0" THEN [Dividend] = "10"; Test Cases: Test # Dividend Divisor Expected Output 1 10 0 Error: Division by zero 2 -10 1 -10.0 3 0 -5 0.0 4 1000 5 200.0 5 10 100 0.1 Example 2: E-commerce Checkout User Request: "Design tests for checkout flow with payment methods, shipping options, and user types." Analysis: Payment: Credit Card, PayPal, Bank Transfer (limited by user type) Shipping: Standard, Express, Overnight User: Guest, Registered, Premium Constraints: Guests can't use Bank Transfer, Premium users get free Express PICT Model: PaymentMethod: CreditCard, PayPal, BankTransfer ShippingMethod: Standard, Express, Overnight UserType: Guest, Registered, Premium IF [UserType] = "Guest" THEN [PaymentMethod] <> "BankTransfer"; IF [UserType] = "Premium" AND [ShippingMethod] = "Express" THEN [PaymentMethod] IN {CreditCard, PayPal}; Output: 12-15 test cases covering all valid payment/shipping/user combinations with expected costs and outcomes.

pict-test-designer

安装