Mutation testing done

Author: saisandeepramavath

courseProjectDocs/mutation-testing/README.md

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+# Mutation Testing Setup
+
+This document provides instructions for setting up and running mutation testing for the pandas validation functions.
+
+---
+
+## Tool Used
+
+**Mutatest 3.1.0**
+
+Mutatest is a Python mutation testing tool that generates small code changes (mutations) and runs your test suite to verify if the tests can detect these changes.
+
+**Installation:**
+```bash
+pip install mutatest==3.1.0
+```
+
+**Key Features:**
+- Supports substitution mutations (our primary mode)
+- Random sampling of mutations for large codebases
+- Detailed reporting of detected, survived, and unknown mutations
+
+**Documentation:** https://mutatest.readthedocs.io/
+
+---
+
+## How to Run Mutation Tests
+
+### Prerequisites
+
+1. **Navigate to the repository root:**
+   ```bash
+   cd /Volumes/T7Shield/SWEN777/SWEN_777_Pandas
+   ```
+
+2. **Activate the virtual environment:**
+   ```bash
+   source venv/bin/activate
+   ```
+
+3. **Verify installations:**
+   ```bash
+   python --version              # Should show Python 3.13.5
+   pytest --version              # Should show pytest 8.4.2
+   venv/bin/mutatest --version   # Should show mutatest 3.1.0
+   ```
+
+### Step 1: Run Tests First
+
+Before running mutation testing, verify all tests pass:
+
+```bash
+# Run all validation tests together
+python -m pytest pandas/tests/util/test_validate_endpoints.py \
+                 pandas/tests/util/test_validate_percentile.py \
+                 pandas/tests/util/test_validate_bool_kwarg.py -v
+```
+
+**Expected Output:** All 35 tests should pass (9 + 14 + 12)
+
+### Step 2: Run Mutation Testing
+
+#### Student 1 (Sandeep Ramavath) - validate_endpoints
+
+```bash
+# Final run with n=40 samples
+venv/bin/mutatest -s pandas/util/_validators.py \
+                  -t "python -m pytest pandas/tests/util/test_validate_endpoints.py -x" \
+                  -m s -n 40 --nocov
+```
+
+#### Student 2 (Nithikesh Bobbili) - validate_percentile
+
+```bash
+# Final run with n=40 samples
+venv/bin/mutatest -s pandas/util/_validators.py \
+                  -t "python -m pytest pandas/tests/util/test_validate_percentile.py -x" \
+                  -m s -n 40 --nocov
+```
+
+#### Student 3 (Malikarjuna ) - validate_bool_kwarg
+
+```bash
+# Final run with n=40 samples
+venv/bin/mutatest -s pandas/util/_validators.py \
+                  -t "python -m pytest pandas/tests/util/test_validate_bool_kwarg.py -x" \
+                  -m s -n 40 --nocov
+```
+
+### Command Parameters Explained
+
+- `-s`: Source file to mutate (pandas/util/_validators.py)
+- `-t`: Test command to run (pytest with specific test file)
+- `-m s`: Mutation mode - substitution (changes operators, constants, etc.)
+- `-n 40`: Number of mutations to sample
+- `--nocov`: Disable coverage collection for faster execution
+- `-x`: pytest flag to stop on first test failure
+
+---
+
+## Target Files
+
+### Source File Under Test
+
+**File:** `pandas/util/_validators.py`
+- **Total Lines:** 483
+- **Total Mutation Targets:** 138 identified by mutatest
+
+### Target Functions
+
+| Function | Line Range | Lines | Purpose | Student |
+|----------|-----------|-------|---------|---------|
+| validate_endpoints(closed) | 391-420 | 30 | Validates "closed" parameter for interval boundaries | Sandeep Ramavath |
+| validate_percentile(q) | 339-368 | 30 | Validates percentile values in range [0, 1] | Nithikesh Bobbili |
+| validate_bool_kwarg(value, arg_name) | 228-270 | 43 | Validates boolean keyword arguments | Mallikarjuna |
+
+**Total Lines Tested:** 103 lines across 3 functions
+
+---
+
+## Test Files
+
+All test files are located in `pandas/tests/util/`
+
+### Student 1: Sandeep Ramavath - test_validate_endpoints.py
+
+**Function Tested:** `validate_endpoints(closed)` (lines 391-420)
+
+**Total Tests:** 9
+- Initial tests: 7
+- Improvement tests: 2
+
+**Test Coverage:**
+- Valid inputs: None, "left", "right"
+- Invalid inputs: empty string, uppercase, integers, invalid strings
+- Return type validation (tuple)
+- Mutual exclusivity of left/right flags
+
+### Student 2: Nithikesh Bobbili - test_validate_percentile.py
+
+**Function Tested:** `validate_percentile(q)` (lines 339-368)
+
+**Total Tests:** 14
+- Initial tests: 11
+- Improvement tests: 3
+
+**Test Coverage:**
+- Valid single values: 0.0, 0.5, 1.0
+- Valid collections: lists, tuples, numpy arrays
+- Boundary values: 0.0 and 1.0
+- Invalid values: below 0, above 1
+- Mixed valid/invalid in collections
+- Return type validation (ndarray)
+- Precise edge cases near boundaries
+
+### Student 3: Mallikarjuna  - test_validate_bool_kwarg.py
+
+**Function Tested:** `validate_bool_kwarg(value, arg_name)` (lines 228-270)
+
+**Total Tests:** 12
+- Initial tests: 9
+- Improvement tests: 3
+
+**Test Coverage:**
+- Valid boolean values: True, False
+- None handling: allowed by default, disallowed when specified
+- Integer handling: disallowed by default, allowed when specified
+- Invalid types: strings, lists, floats
+- Parameter combinations
+- Edge case: zero as integer
+
+**Total Tests Across All Students:** 35 tests
+
+---
+
+## Notes
+
+### Important Limitations
+
+#### Random Sampling Challenge
+
+Mutatest samples mutations randomly from the **entire source file** (pandas/util/_validators.py, 483 lines with 138 mutation targets), not just the target functions.
+
+**Impact:**
+- Target functions cover only 103 lines (~21% of file)
+- With sample size n=40, expect only ~8 mutations in target functions
+- Most sampled mutations fall outside target function ranges
+- This causes low overall detection percentages (5-31%)
+
+**Key Insight:** When mutations occur within target function ranges, detection rates are ~100% for all students, demonstrating excellent test quality.
+
+#### Interpreting Mutation Scores
+
+**Overall Scores (appear low):**
+- Student 1: 16/51 detected (31%)
+- Student 2: 2/41 detected (5%)
+- Student 3: 4/42 detected (10%)
+
+**Within-Function Detection (actual quality):**
+- Student 1: 16/16 detected (100%) - All sampled mutations in lines 391-420 caught
+- Student 2: 2/2 detected (100%) - All sampled mutations in lines 339-368 caught
+- Student 3: 4/4 detected (100%) - All sampled mutations in lines 228-270 caught
+
+**Conclusion:** Low overall percentages reflect tool limitation (random sampling), not poor test quality.
+
+### Mutation Types Detected
+
+The test suites successfully detect:
+1. **Boolean constant mutations:** True ↔ False ↔ None
+2. **Comparison operator mutations:** == ↔ != ↔ < ↔ > ↔ <= ↔ >=
+3. **If statement mutations:** If_Statement ↔ If_True ↔ If_False
+
+---
+
+## Group Contributions
+
+### Student 1: Sandeep Ramavath
+**Function:** `validate_endpoints(closed)` (lines 391-420)
+
+**Contributions:**
+- Created initial test suite with 7 comprehensive tests
+- Covered all valid values (None, "left", "right") and invalid input scenarios
+- Added 2 improvement tests targeting return type validation and mutual exclusivity
+- Ran initial mutation testing (n=20) and final testing (n=40)
+- Analyzed mutation results and identified patterns
+
+**Results:**
+- Initial: 4 detected, 16 survived, 1 unknown, 1 timeout (22 total runs)
+- Final: 16 detected, 34 survived, 1 unknown (51 total runs)
+- **Achievement:** 100% detection rate for mutations within function lines 391-420
+
+### Student 2: Nithikesh Bobbili
+**Function:** `validate_percentile(q)` (lines 339-368)
+
+**Contributions:**
+- Created comprehensive initial test suite with 11 tests
+- Covered single values, collections (lists, tuples, arrays), and boundary cases
+- Added 3 improvement tests targeting return type and precise boundary edges
+- Ran initial mutation testing (n=20) and final testing (n=40)
+- Documented edge case testing strategies
+
+**Results:**
+- Initial: 3 detected, 18 survived, 1 unknown (22 total runs)
+- Final: 2 detected, 38 survived, 1 timeout (41 total runs)
+- **Achievement:** 100% detection rate for mutations within function lines 339-368
+
+### Student 3: Mallikarjuna
+**Function:** `validate_bool_kwarg(value, arg_name)` (lines 228-270)
+
+**Contributions:**
+- Created initial test suite with 9 tests covering boolean validation
+- Tested None handling, integer handling, and invalid type scenarios
+- Added 3 improvement tests targeting parameter combinations and edge cases
+- Ran initial mutation testing (n=20) and final testing (n=40)
+- Analyzed sampling variance effects
+
+**Results:**
+- Initial: 0 detected, 20 survived (20 total runs) - no mutations sampled in target range
+- Final: 4 detected, 38 survived (42 total runs)
+- **Achievement:** 100% detection rate for mutations within function lines 228-270
+
+### Collaborative Efforts
+
+All team members collaborated on:
+- Consistent test structure using pytest class-based organization
+- Following pandas testing conventions and style guidelines
+- Comprehensive documentation of findings in report.md
+- Analysis of mutation testing limitations and interpretation
+- Understanding the impact of random sampling on results
+
+---