Create testting.md

testting.md

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+# Unit test and validation and integration 
+
+We can create specific unit and integration tests to ensure that each stage of your data pipeline works correctly. Below are examples of tests that could be written for each of the critical components in the project:
+
+### 1. **Unit Tests for `web_scraping` scripts**
+
+#### Test: `test_adzuna_scraper.py`
+This test ensures that the `adzuna.py` scraper correctly fetches and processes data.
+
+```python
+import unittest
+from scripts.web_scraping.adzuna import fetch_adzuna_data
+
+class TestAdzunaScraper(unittest.TestCase):
+    def test_fetch_adzuna_data(self):
+        # Assuming fetch_adzuna_data returns a list of dictionaries
+        data = fetch_adzuna_data()
+        self.assertIsInstance(data, list)
+        self.assertGreater(len(data), 0)
+        for job in data:
+            self.assertIn('title', job)
+            self.assertIn('company', job)
+            self.assertIn('location', job)
+            self.assertIn('salary', job)
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+#### Test: `test_muse_scraper.py`
+Similar to the Adzuna test, but for the Muse data.
+
+```python
+import unittest
+from scripts.web_scraping.muse import fetch_muse_data
+
+class TestMuseScraper(unittest.TestCase):
+    def test_fetch_muse_data(self):
+        data = fetch_muse_data()
+        self.assertIsInstance(data, list)
+        self.assertGreater(len(data), 0)
+        for job in data:
+            self.assertIn('title', job)
+            self.assertIn('company', job)
+            self.assertIn('location', job)
+            self.assertIn('salary', job)
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 2. **Unit Tests for `etl` script**
+
+#### Test: `test_etl_process.py`
+This test ensures that the ETL process correctly processes the scraped data.
+
+```python
+import unittest
+from scripts.etl.etlscript import process_data
+
+class TestETLProcess(unittest.TestCase):
+    def test_process_data(self):
+        # Mocked raw data
+        raw_data = [{'title': 'Data Scientist', 'company': 'Company A', 'location': 'Berlin', 'salary': '60,000'}]
+        processed_data = process_data(raw_data)
+
+        self.assertIsInstance(processed_data, list)
+        self.assertGreater(len(processed_data), 0)
+        for job in processed_data:
+            self.assertIn('title', job)
+            self.assertIn('company', job)
+            self.assertIn('location', job)
+            self.assertIn('salary', job)
+            self.assertIsInstance(job['salary'], int)  # Assuming ETL converts salary to an integer
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 3. **Unit Tests for `database` connection**
+
+#### Test: `test_db_connection.py`
+This test checks that the database connection is established correctly.
+
+```python
+import unittest
+from scripts.database.db_connection import create_connection
+
+class TestDBConnection(unittest.TestCase):
+    def test_create_connection(self):
+        conn = create_connection()
+        self.assertIsNotNone(conn)
+        self.assertTrue(conn.is_connected())
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 4. **Integration Tests**
+
+#### Test: `test_full_pipeline.py`
+This test ensures that data flows correctly through the entire pipeline, from scraping to processing to loading.
+
+```python
+import unittest
+from scripts.web_scraping.adzuna import fetch_adzuna_data
+from scripts.etl.etlscript import process_data
+from scripts.database.db_connection import create_connection, load_data_to_db
+
+class TestFullPipeline(unittest.TestCase):
+    def test_full_pipeline(self):
+        raw_data = fetch_adzuna_data()
+        self.assertGreater(len(raw_data), 0)
+
+        processed_data = process_data(raw_data)
+        self.assertGreater(len(processed_data), 0)
+
+        conn = create_connection()
+        success = load_data_to_db(conn, processed_data)
+        self.assertTrue(success)
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 5. **Performance Testing**
+
+#### Test: `test_pipeline_performance.py`
+This test checks that the data pipeline performs efficiently.
+
+```python
+import timeit
+from scripts.web_scraping.adzuna import fetch_adzuna_data
+from scripts.etl.etlscript import process_data
+from scripts.database.db_connection import create_connection, load_data_to_db
+
+def test_pipeline_performance():
+    setup_code = """
+from scripts.web_scraping.adzuna import fetch_adzuna_data
+from scripts.etl.etlscript import process_data
+from scripts.database.db_connection import create_connection, load_data_to_db
+    """
+    test_code = """
+raw_data = fetch_adzuna_data()
+processed_data = process_data(raw_data)
+conn = create_connection()
+load_data_to_db(conn, processed_data)
+    """
+    execution_time = timeit.timeit(stmt=test_code, setup=setup_code, number=1)
+    assert execution_time < 5  # Arbitrary threshold
+
+if __name__ == '__main__':
+    test_pipeline_performance()
+```
+
+### 6. **CI/CD Integration**
+
+In your `ci.yml` file, you should ensure these tests run automatically with each commit. Below is a sample configuration for running these tests using GitHub Actions:
+
+```yaml
+name: CI Pipeline
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Run tests
+      run: |
+        pytest
+```
+
+### Summary
+
+These tests cover key aspects of your data pipeline, ensuring each stage from web scraping to ETL to database storage works correctly. The integration of these tests into a CI/CD pipeline will ensure that any changes to your project are thoroughly vetted before deployment, minimizing the risk of errors in production. This approach can be applied to other components of your project as well, ensuring robust and reliable operation.