Perform risk analysis to prioritize test cases

Preconditions (2)

Step 1: Review the list of features and their risk factors

Step 2: Calculate a risk score for each feature by multiplying impact and likelihood

Step 3: Sort the features by their risk score in descending order

Step 4: Select the top 3 features with the highest risk scores

Step 5: Prepare test cases focusing on these high-risk features

✅ Expected Result: The top 3 features with the highest risk scores are correctly identified and prioritized for testing

Automation Requirements - Python unittest

Assertions Needed:

Verify risk scores are calculated correctly for each feature

Verify features are sorted correctly by risk score descending

Verify the top 3 features selected match expected high-risk features

Best Practices:

Use clear and descriptive variable names

Separate calculation logic from test assertions

Use setup methods to prepare test data

Keep tests independent and repeatable

Automated Solution

Testing Fundamentals

import unittest

class RiskAnalysis:
    def __init__(self, features):
        self.features = features  # List of dicts with 'name', 'impact', 'likelihood'

    def calculate_risk_scores(self):
        for feature in self.features:
            feature['risk_score'] = feature['impact'] * feature['likelihood']
        return self.features

    def get_top_risk_features(self, top_n=3):
        scored = self.calculate_risk_scores()
        sorted_features = sorted(scored, key=lambda f: f['risk_score'], reverse=True)
        return sorted_features[:top_n]

class TestRiskAnalysis(unittest.TestCase):
    def setUp(self):
        self.features = [
            {'name': 'Login', 'impact': 5, 'likelihood': 4},
            {'name': 'Payment', 'impact': 9, 'likelihood': 3},
            {'name': 'Search', 'impact': 4, 'likelihood': 2},
            {'name': 'Profile Update', 'impact': 3, 'likelihood': 5},
            {'name': 'Notifications', 'impact': 2, 'likelihood': 1}
        ]
        self.risk_analysis = RiskAnalysis(self.features)

    def test_calculate_risk_scores(self):
        scored = self.risk_analysis.calculate_risk_scores()
        expected_scores = {
            'Login': 20,
            'Payment': 27,
            'Search': 8,
            'Profile Update': 15,
            'Notifications': 2
        }
        for feature in scored:
            self.assertEqual(feature['risk_score'], expected_scores[feature['name']])

    def test_get_top_risk_features(self):
        top_features = self.risk_analysis.get_top_risk_features()
        top_names = [f['name'] for f in top_features]
        expected_top = ['Payment', 'Login', 'Profile Update']
        self.assertEqual(top_names, expected_top)

if __name__ == '__main__':
    unittest.main()

This code defines a RiskAnalysis class that calculates risk scores by multiplying impact and likelihood for each feature. It then sorts features by risk score descending and selects the top 3.

The TestRiskAnalysis class uses Python's unittest framework. The setUp method prepares test data with features and their risk factors.

The first test test_calculate_risk_scores checks if risk scores are calculated correctly for each feature.

The second test test_get_top_risk_features verifies that the top 3 features selected are the ones with the highest risk scores.

This separation keeps calculation logic and assertions clear. The tests are independent and repeatable.

Common Mistakes - 4 Pitfalls

Hardcoding risk scores instead of calculating them

Sorting features in ascending order instead of descending

Mixing test logic with production code

Not using setup methods to prepare test data

Bonus Challenge

Now add data-driven testing with 3 different sets of features and risk factors

Show Hint