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"""
Directional Tests for Skill Classification Model
These tests verify that specific changes to the input lead to PREDICTABLE changes
in the model's predictions. For example:
- Adding skill-specific keywords should increase confidence in related skills
- Removing domain-specific terms should decrease confidence in those domains
- Adding context about a technology should add related skill predictions
Based on Ribeiro et al. (2020) "Beyond Accuracy: Behavioral Testing of NLP models"
"""
import pytest
import numpy as np
@pytest.mark.directional
class TestDirectional:
"""Test suite for directional expectations of the model."""
def test_adding_language_keyword(self, predict_with_labels, predict_text):
"""
Test that adding programming language keywords increases language-related predictions.
Adding "Java" or "Python" should make language skills more likely.
"""
base = "Fixed bug in authentication system"
with_java = "Fixed bug in Java authentication system"
with_python = "Fixed bug in Python authentication system"
pred_base = set(predict_with_labels(base))
pred_java = set(predict_with_labels(with_java))
pred_python = set(predict_with_labels(with_python))
# Check if language-related labels appear (depends on your label schema)
# Note: Adjust these checks based on actual labels in your dataset
print(f"\nBase predictions: {pred_base}")
print(f"With Java: {pred_java}")
print(f"With Python: {pred_python}")
# At minimum, predictions should not become drastically worse
# It's acceptable if predictions stay the same (model might already predict Language)
assert len(pred_java) >= len(pred_base) * 0.5, (
"Adding Java should not drastically reduce predictions"
)
assert len(pred_python) >= len(pred_base) * 0.5, (
"Adding Python should not drastically reduce predictions"
)
def test_adding_data_structure_keyword(self, predict_with_labels):
"""
Test that adding data structure keywords increases data structure predictions.
"""
base = "Implemented search functionality"
with_hashmap = "Implemented search functionality using HashMap"
with_tree = "Implemented search functionality using binary tree"
pred_base = set(predict_with_labels(base))
pred_hashmap = set(predict_with_labels(with_hashmap))
pred_tree = set(predict_with_labels(with_tree))
print(f"\nBase: {pred_base}")
print(f"With HashMap: {pred_hashmap}")
print(f"With Tree: {pred_tree}")
# Adding data structures should increase related predictions
# pred_hashmap and pred_tree should have more or different labels than base
assert len(pred_hashmap) >= len(pred_base) * 0.8, (
"Adding HashMap should not drastically reduce predictions"
)
assert len(pred_tree) >= len(pred_base) * 0.8, (
"Adding tree should not drastically reduce predictions"
)
def test_adding_error_handling_context(self, predict_with_labels):
"""
Test that adding error handling keywords increases error handling predictions.
"""
base = "Updated user login flow"
with_exception = "Updated user login flow with exception handling"
with_try_catch = "Updated user login flow with try-catch blocks"
pred_base = set(predict_with_labels(base))
pred_exception = set(predict_with_labels(with_exception))
pred_try_catch = set(predict_with_labels(with_try_catch))
print(f"\nBase: {pred_base}")
print(f"With exception: {pred_exception}")
print(f"With try-catch: {pred_try_catch}")
# Error handling keywords should not drastically reduce predictions
# Check if "Error Handling" is in predictions (likely already there)
has_error_handling = any("Error" in label or "Exception" in label
for label in pred_exception | pred_try_catch)
assert len(pred_exception) >= len(pred_base) * 0.5, (
"Adding error handling context should not drastically reduce predictions"
)
# At least one prediction should contain error-related terms
print(f"Has error handling related labels: {has_error_handling}")
def test_removing_specific_technology(self, predict_text):
"""
Test that removing technology-specific keywords reduces related predictions.
"""
with_tech = "Fixed database connection pooling issue in PostgreSQL"
without_tech = "Fixed database connection pooling issue"
pred_with = predict_text(with_tech)
pred_without = predict_text(without_tech)
# Predictions should differ when removing specific technology
# The version with specific tech should generally have same or more predictions
assert len(pred_with) >= len(pred_without) * 0.7, (
"Removing technology specifics should not drastically increase predictions"
)
def test_adding_api_context(self, predict_with_labels):
"""
Test that adding API-related keywords increases API/web service predictions.
"""
base = "Fixed user authentication"
with_api = "Fixed user authentication REST API endpoint"
with_graphql = "Fixed user authentication GraphQL endpoint"
pred_base = set(predict_with_labels(base))
pred_api = set(predict_with_labels(with_api))
pred_graphql = set(predict_with_labels(with_graphql))
print(f"\nBase: {pred_base}")
print(f"With REST API: {pred_api}")
print(f"With GraphQL: {pred_graphql}")
# API keywords should not drastically reduce predictions
assert len(pred_api) >= len(pred_base) * 0.5, (
"Adding REST API should not drastically reduce predictions"
)
assert len(pred_graphql) >= len(pred_base) * 0.5, (
"Adding GraphQL should not drastically reduce predictions"
)
def test_adding_testing_keywords(self, predict_with_labels):
"""
Test that adding testing-related keywords increases testing skill predictions.
"""
base = "Implemented new feature for user management"
with_tests = "Implemented new feature for user management with unit tests"
with_integration = "Implemented new feature for user management with integration tests"
pred_base = set(predict_with_labels(base))
pred_unit = set(predict_with_labels(with_tests))
pred_integration = set(predict_with_labels(with_integration))
print(f"\nBase: {pred_base}")
print(f"With unit tests: {pred_unit}")
print(f"With integration tests: {pred_integration}")
# Testing keywords should not drastically reduce predictions
# Check if testing-related labels are present
has_testing = any("Test" in label or "Automated" in label
for label in pred_unit | pred_integration)
assert len(pred_unit) >= len(pred_base) * 0.5, (
"Adding testing keywords should not drastically reduce predictions"
)
print(f"Has testing related labels: {has_testing}")
def test_adding_performance_keywords(self, predict_with_labels):
"""
Test that adding performance-related keywords affects predictions.
"""
base = "Optimized search algorithm"
with_perf = "Optimized search algorithm for better performance and reduced memory usage"
with_cache = "Optimized search algorithm with caching"
pred_base = set(predict_with_labels(base))
pred_perf = set(predict_with_labels(with_perf))
pred_cache = set(predict_with_labels(with_cache))
print(f"\nBase: {pred_base}")
print(f"With performance: {pred_perf}")
print(f"With caching: {pred_cache}")
# Performance keywords should affect predictions
# More specific descriptions should generally maintain or add labels
assert len(pred_perf) >= len(pred_base) * 0.7, (
"Adding performance context should not drastically reduce predictions"
)
def test_adding_security_context(self, predict_with_labels):
"""
Test that adding security keywords increases security-related predictions.
"""
base = "Updated authentication system"
with_security = "Updated authentication system with OAuth2 security"
with_encryption = "Updated authentication system with password encryption"
pred_base = set(predict_with_labels(base))
pred_oauth = set(predict_with_labels(with_security))
pred_encryption = set(predict_with_labels(with_encryption))
print(f"\nBase: {pred_base}")
print(f"With OAuth: {pred_oauth}")
print(f"With encryption: {pred_encryption}")
# Security keywords should not drastically reduce predictions
# Authentication is already security-related, so predictions should be stable
assert len(pred_oauth) >= len(pred_base) * 0.5, (
"Adding OAuth2 should not drastically reduce predictions"
)
assert len(pred_encryption) >= len(pred_base) * 0.5, (
"Adding encryption should not drastically reduce predictions"
)
def test_adding_devops_keywords(self, predict_with_labels):
"""
Test that adding DevOps keywords increases DevOps-related predictions.
"""
base = "Deployed new version"
with_docker = "Deployed new version using Docker containers"
with_ci = "Deployed new version through CI/CD pipeline"
pred_base = set(predict_with_labels(base))
pred_docker = set(predict_with_labels(with_docker))
pred_ci = set(predict_with_labels(with_ci))
print(f"\nBase: {pred_base}")
print(f"With Docker: {pred_docker}")
print(f"With CI/CD: {pred_ci}")
# DevOps keywords should not drastically reduce predictions
# Check if DevOps-related labels are present
has_devops = any("DevOps" in label or "Operations" in label or "Deployment" in label
for label in pred_docker | pred_ci | pred_base)
assert len(pred_docker) >= len(pred_base) * 0.5, (
"Adding Docker should not drastically reduce predictions"
)
print(f"Has DevOps related labels: {has_devops}")
def test_increasing_technical_detail(self, predict_text):
"""
Test that adding more technical detail generally increases or maintains predictions.
More specific descriptions should not drastically reduce the number of relevant skills.
"""
vague = "Fixed bug"
specific = "Fixed null pointer exception in user service layer"
very_specific = "Fixed null pointer exception in UserService.getUserById() method when handling deleted users"
pred_vague = predict_text(vague)
pred_specific = predict_text(specific)
pred_very_specific = predict_text(very_specific)
print(f"\nVague ({len(pred_vague)} labels): {pred_vague}")
print(f"Specific ({len(pred_specific)} labels): {pred_specific}")
print(f"Very specific ({len(pred_very_specific)} labels): {pred_very_specific}")
# More detail should generally add relevant skills, not remove them drastically
# Allow some variance since very specific text might lose some general predictions
assert len(pred_specific) >= len(pred_vague) * 0.5, (
"Adding technical detail should not reduce predictions drastically"
)
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