Calculate and verify defect metrics from a defect log

Preconditions (2)

Step 1: Open the defect log file

Step 2: Count total number of defects

Step 3: Count number of open defects

Step 4: Count number of closed defects

Step 5: Calculate defect density given total defects and total lines of code (LOC)

Step 6: Calculate defect removal efficiency (DRE) using defects found before release and total defects

Step 7: Verify that the calculated metrics match expected values

✅ Expected Result: The defect metrics (total defects, open defects, closed defects, defect density, DRE) are correctly calculated and match the expected values

Automation Requirements - Python unittest

Assertions Needed:

Assert total defects count equals expected

Assert open defects count equals expected

Assert closed defects count equals expected

Assert defect density calculation is correct

Assert defect removal efficiency calculation is correct

Best Practices:

Use setup method to load defect data

Use clear variable names for metrics

Use assertEqual for exact matches

Keep test focused on metric calculations

Avoid hardcoding values inside test methods

Automated Solution

Testing Fundamentals

import unittest

class DefectMetricsTest(unittest.TestCase):
    def setUp(self):
        # Sample defect log data: list of dicts
        self.defects = [
            {'id': 'D1', 'severity': 'High', 'status': 'Open', 'reported_date': '2024-01-01', 'found_before_release': True},
            {'id': 'D2', 'severity': 'Medium', 'status': 'Closed', 'reported_date': '2024-01-02', 'found_before_release': True},
            {'id': 'D3', 'severity': 'Low', 'status': 'Closed', 'reported_date': '2024-01-03', 'found_before_release': False},
            {'id': 'D4', 'severity': 'High', 'status': 'Open', 'reported_date': '2024-01-04', 'found_before_release': True},
            {'id': 'D5', 'severity': 'Medium', 'status': 'Closed', 'reported_date': '2024-01-05', 'found_before_release': False}
        ]
        self.total_loc = 1000  # total lines of code

    def test_defect_metrics(self):
        total_defects = len(self.defects)
        open_defects = sum(1 for d in self.defects if d['status'] == 'Open')
        closed_defects = sum(1 for d in self.defects if d['status'] == 'Closed')

        defect_density = total_defects / self.total_loc  # defects per LOC

        defects_found_before_release = sum(1 for d in self.defects if d['found_before_release'])
        # Defect Removal Efficiency (DRE) = defects found before release / total defects
        dre = defects_found_before_release / total_defects

        # Expected values based on sample data
        expected_total_defects = 5
        expected_open_defects = 2
        expected_closed_defects = 3
        expected_defect_density = 5 / 1000
        expected_dre = 3 / 5

        self.assertEqual(total_defects, expected_total_defects, "Total defects count mismatch")
        self.assertEqual(open_defects, expected_open_defects, "Open defects count mismatch")
        self.assertEqual(closed_defects, expected_closed_defects, "Closed defects count mismatch")
        self.assertAlmostEqual(defect_density, expected_defect_density, places=5, msg="Defect density mismatch")
        self.assertAlmostEqual(dre, expected_dre, places=5, msg="Defect Removal Efficiency mismatch")

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

This test uses Python's unittest framework to automate defect metrics calculation.

The setUp method prepares sample defect data and total lines of code.

The test method calculates total defects, open and closed defects counts, defect density, and defect removal efficiency (DRE).

Assertions check that these calculated values match expected values derived from the sample data.

Using assertEqual and assertAlmostEqual ensures exact and approximate matches respectively.

This approach keeps the test clear, focused, and easy to maintain.

Common Mistakes - 4 Pitfalls

Hardcoding expected values inside the test method without explanation

Not using setup method to prepare test data

Using print statements instead of assertions

Calculating metrics incorrectly, e.g., dividing by zero or wrong denominator

Bonus Challenge

Now add data-driven testing with 3 different defect logs having different defect counts and LOC values

Show Hint