Tests PyPict
Concevez des cas de test en utilisant les principes du test par paires pour générer des jeux de tests minimaux.
SKILL.md Definition
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:
- 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";
```
## Generated Test Cases
| Test # | Parameter1 | Parameter2 | Expected Output |
| --- | --- | --- | --- |
| 1 | Value1 | ValueA | Success |
| 2 | Value2 | ValueB | Success |
| 3 | Value1 | ValueB | Error: Invalid combination |
...
## 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, Largeinstead ofFileSize: 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
NOTor<>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.
Skills en vedette
"Trouvez les 'agent skills' parfaits pour votre projet"
Base de données ZINC
Base de données sélectionnée de composés commerciaux pour le criblage virtuel.
Zarr Python
Implémentation Python de tableaux N-dimensionnels compressés et partitionnés pour les données scientifiques.
Base de données USPTO
Accès à la base de données de l'Office des brevets et des marques des États-Unis.
Base de données UniProt
Ressource complète, de haute qualité et gratuite pour les séquences protéiques et les informations fonctionnelles.
Puissants Agent Skills
Boostez les performances de votre IA avec notre collection de compétences professionnelles.
Prêt à l'emploi
Copiez et collez dans n'importe quel système d'agent supportant les skills.
Conception modulaire
Combinez les 'code skills' pour créer des comportements d'agent complexes.
Optimisé
Chaque 'agent skill' est réglé pour une performance et une précision élevées.
Open Source
Tous les 'code skills' sont ouverts aux contributions et à la personnalisation.
Multiplateforme
Fonctionne avec divers LLM et frameworks d'agents.
Sûr et sécurisé
Compétences vérifiées qui suivent les meilleures pratiques de sécurité IA.
Renforcez vos agents
Commencez à utiliser Agiskills dès aujourd'hui et voyez la différence.
Explorer maintenantComment ça marche
Commencez avec les agent skills en trois étapes simples.
Choisir un Skill
Trouvez le skill dont vous avez besoin dans notre collection.
Lire la Doc
Comprenez le fonctionnement du skill et ses contraintes.
Copier & Utiliser
Collez la définition dans la configuration de votre agent.
Tester
Vérifiez les résultats et affinez si nécessaire.
Déployer
Lancez votre agent IA spécialisé.
Ce que disent les développeurs
Voyez pourquoi les développeurs du monde entier choisissent Agiskills.
Alex Smith
Ingénieur IA
"Agiskills a complètement changé ma façon de construire des agents IA."
Maria Garcia
Chef de produit
"La skill PDF Specialist a résolu pour nous des problèmes complexes d'analyse de documents."
John Doe
Développeur
"Compétences professionnelles et bien documentées. Je recommande vivement !"
Sarah Lee
Artiste
"La compétence Art algorithmique produit un code incroyablement beau."
Chen Wei
Spécialiste Frontend
"Les thèmes générés par Theme Factory sont parfaits au pixel près."
Robert T.
CTO
"Nous utilisons désormais Agiskills comme standard pour notre équipe IA."
FAQ
Tout ce que vous devez savoir sur Agiskills.
Oui, toutes les compétences publiques peuvent être copiées et utilisées gratuitement.