Updated report

Author: MALLI7622

courseProjectDocs/integration-testing/report.md

@@ -27,3 +27,100 @@ This integration testing effort focuses on verifying interactions between multip
 1. **validate_fillna_kwargs with clean_fill_method**: Tests delegation from validator to missing data module for method normalization
 2. **Series.fillna/ffill operations**: Tests complete pipeline from user API through validation to missing data handling
 
+### Test 3: Dtype Backend-Libs Integration (Mallikarjuna)
+**Modules Integrated:**
+- `pandas.util._validators` (validation functions)
+- `pandas._libs.lib` (C extension library with sentinel values)
+- `numpy` (array handling and validation)
+
+**Interactions Tested:**
+1. **check_dtype_backend with lib.no_default**: Tests validator interaction with C library sentinel values
+2. **validate_percentile with numpy arrays**: Tests pandas validation with numpy array conversion and bounds checking
+
+## Test Data Preparation
+
+### Input Data Generation
+
+**Test 1 - Series/DataFrame Integration:**
+- **Input**: Created Series with explicit dtype (`int32`) and sample data `[1, 2, 3]`
+- **Input**: Created multiple Series with different dtypes: int64, float32, object
+- **Rationale**: Different dtypes exercise type preservation logic across module boundaries
+
+**Test 2 - Validation/Missing Data:**
+- **Input**: Series with `np.nan` values: `[1.0, np.nan, 3.0, np.nan, 5.0]`
+- **Input**: Method names `"pad"`, `"ffill"` and `None` values
+- **Rationale**: Missing values and various method names test validation and fill method delegation
+
+**Test 3 - Backend/Libs Validation:**
+- **Input**: `lib.no_default` sentinel, valid backends (`"numpy_nullable"`, `"pyarrow"`), invalid backend string
+- **Input**: Valid percentiles (`0.5`, `[0.25, 0.5, 0.75]`) and invalid (`1.5`, `[0.25, 1.5, 0.75]`)
+- **Rationale**: Mix of valid/invalid inputs tests error handling across module boundaries
+
+### Expected Output Data
+
+All tests include explicit expected outputs:
+- Series/DataFrame tests verify dtype preservation and data integrity
+- Validation tests verify normalized method names and appropriate ValueError exceptions
+- Backend tests verify acceptance of valid values and rejection with specific error messages
+
+## Execution and Results
+
+**Test File**: `pandas/tests/util/test_integration.py`
+
+**Execution Command:**
+```bash
+python -m pytest pandas/tests/util/test_integration.py -v
+```
+
+**Test Results:**
+```
+collected 6 items
+
+test_series_to_dataframe_dtype_preservation PASSED
+test_dataframe_from_dict_mixed_series_dtypes PASSED
+test_validate_fillna_with_clean_method PASSED
+test_series_fillna_integration PASSED
+test_check_dtype_backend_with_lib_sentinel PASSED
+test_percentile_validation_with_numpy_arrays PASSED
+
+=================================== 6 passed in 0.94s
+```
+
+**Summary:**
+- **Total Tests**: 6 integration tests
+- **Passed**: 6 (100%)
+- **Failed**: 0
+- **Execution Time**: 0.94 seconds
+
+### Defects Discovered
+
+**No defects were discovered during integration testing.** All module interactions functioned as expected:
+
+- Series-to-DataFrame conversion preserves dtypes correctly
+- DataFrame construction handles mixed-dtype Series properly
+- Validation module correctly delegates to missing data module
+- Series fillna operations integrate validation and missing data modules
+- Backend validation properly handles C library sentinel values
+- Percentile validation correctly integrates with NumPy array handling
+
+All error cases (ValueError for invalid inputs) behaved as designed, raising appropriate exceptions with descriptive messages.
+
+## Bug Reports
+
+**No bugs identified.** All integration points between modules are functioning correctly. The following expected behaviors were verified:
+
+1. **Type preservation across module boundaries**: Dtypes maintained through Series→DataFrame→Internals conversions
+2. **Validation delegation**: Validators correctly call specialized modules (e.g., `clean_fill_method`)
+3. **Error propagation**: Invalid inputs raise appropriate exceptions with clear messages
+4. **Sentinel value handling**: C library sentinels (`lib.no_default`) recognized by validators
+
+## Group Contributions
+
+| Student | Test Cases | Modules Integrated | Coverage |
+|---------|------------|-------------------|----------|
+| **Sandeep Ramavath** | 2 tests | Series, DataFrame, Internals, Dtypes | Series-DataFrame conversion and construction |
+| **Nithikesh Bobbili** | 2 tests | Validators, Missing Data, Series, Internals | Fillna validation and operation pipeline |
+| **Mallikarjuna** | 2 tests | Validators, C Libs, NumPy | Backend validation and percentile checking |
+
+**Total**: 6 integration tests covering 8+ distinct pandas modules with both normal and edge case scenarios.
+