Initial commit: Randomized Quicksort and Hash Table with Chaining implementation

- Implemented Randomized Quicksort algorithm with performance analysis
- Implemented Hash Table with Chaining for collision resolution
- Added comprehensive test suite (30+ test cases)
- Created test runner script with multiple test options
- Added detailed README with architecture diagrams and documentation
- Added MIT License
- Includes examples and comprehensive documentation

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
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+# C extensions
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+
+# Distribution / packaging
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+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+

LICENSE

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+MIT License
+
+Copyright (c) 2025 MSCS532 Assignment 3
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

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+# Randomized Quicksort & Hash Table with Chaining - Algorithm Efficiency and Scalability
+
+## Overview
+
+This project implements two fundamental algorithms and data structures demonstrating algorithm efficiency and scalability:
+
+1. **Randomized Quicksort Algorithm** - An efficient sorting algorithm with average O(n log n) time complexity
+2. **Hash Table with Chaining** - A hash table implementation using chaining for collision resolution
+
+Both implementations provide comprehensive test suites, performance analysis utilities, and detailed documentation for educational purposes.
+
+### Key Features
+
+* ✅ **Randomized Quicksort**: Efficient sorting with randomized pivot selection to avoid worst-case performance
+* ✅ **Performance Analysis**: Built-in utilities for comparing and analyzing algorithm performance
+* ✅ **Hash Table with Chaining**: Complete hash table implementation with dynamic resizing
+* ✅ **Comprehensive Test Suite**: Extensive test coverage including edge cases, stress tests, and performance benchmarks
+* ✅ **Well-Documented Code**: Clear comments, docstrings, and educational examples
+* ✅ **Production-Ready**: Robust error handling and comprehensive test coverage
+
+## Architecture
+
+### Randomized Quicksort Algorithm Flow
+
+```
+Input Array: [64, 34, 25, 12, 22, 11, 90, 5]
+             ↓
+    ┌─────────────────────────────────────┐
+    │   Randomized Quicksort Process      │
+    └─────────────────────────────────────┘
+             ↓
+    ┌─────────────────────────────────────────────────┐
+    │  Step 1: Randomly select pivot                  │
+    │  Pivot: 25 (randomly selected)                  │
+    │  Partition: [12, 22, 11, 5] | 25 | [64, 34, 90] │
+    └─────────────────────────────────────────────────┘
+             ↓
+    ┌─────────────────────────────────────┐
+    │  Step 2: Recursively sort left      │
+    │  Array: [12, 22, 11, 5]             │
+    │  Pivot: 11 → [5, 11] | [12, 22]     │
+    └─────────────────────────────────────┘
+             ↓
+    ┌─────────────────────────────────────┐
+    │  Step 3: Recursively sort right     │
+    │  Array: [64, 34, 90]                │
+    │  Pivot: 64 → [34, 64] | [90]        │
+    └─────────────────────────────────────┘
+             ↓
+Output Array: [5, 11, 12, 22, 25, 34, 64, 90]
+```
+
+### Hash Table with Chaining Structure
+
+```
+Hash Table (size=8)
+┌─────────────────────────────────────────┐
+│ Bucket 0: [Key: 8, Value: "eight"]      │
+│           [Key: 16, Value: "sixteen"]   │
+│ Bucket 1: [Key: 9, Value: "nine"]       │
+│ Bucket 2: [Key: 10, Value: "ten"]       │
+│           [Key: 18, Value: "eighteen"]  │
+│ Bucket 3: [Key: 11, Value: "eleven"]    │
+│ Bucket 4: [Key: 12, Value: "twelve"]    │
+│ Bucket 5: [Key: 13, Value: "thirteen"]  │
+│ Bucket 6: [Key: 14, Value: "fourteen"]  │
+│ Bucket 7: [Key: 15, Value: "fifteen"]   │
+└─────────────────────────────────────────┘
+         ↓
+    Collision Resolution via Chaining
+    (Multiple keys hash to same bucket)
+```
+
+### Core Algorithm Structure
+
+#### Randomized Quicksort
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│              Randomized Quicksort                           │
+├─────────────────────────────────────────────────────────────┤
+│  Function: randomized_quicksort(arr)                        │
+│  Input: Array of comparable elements                        │
+│  Output: Array sorted in ascending order                    │
+├─────────────────────────────────────────────────────────────┤
+│  Algorithm Steps:                                           │
+│  1. If array has ≤ 1 element, return                        │
+│  2. Randomly select pivot element                           │
+│  3. Partition array around pivot                            │
+│  4. Recursively sort left subarray                          │
+│  5. Recursively sort right subarray                         │
+│  6. Combine results                                         │
+└─────────────────────────────────────────────────────────────┘
+```
+
+#### Hash Table with Chaining
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│              Hash Table with Chaining                       │
+├─────────────────────────────────────────────────────────────┤
+│  Class: HashTable                                           │
+│  Operations: insert, get, delete, contains                  │
+├─────────────────────────────────────────────────────────────┤
+│  Key Operations:                                            │
+│  1. Hash function: h(k) = floor(m × (k × A mod 1))          │
+│  2. Collision resolution: Chaining (linked lists)           │
+│  3. Load factor management: Resize when threshold exceeded  │
+│  4. Dynamic resizing: Double size when load > 0.75          │
+└─────────────────────────────────────────────────────────────┘
+```
+
+## Implementation Details
+
+### Part 1: Randomized Quicksort
+
+#### Core Functions
+
+##### 1. `randomized_quicksort(arr)`
+
+* **Purpose**: Sort array using randomized quicksort algorithm
+* **Parameters**: `arr` (list) - Input array to be sorted
+* **Returns**: `list` - New array sorted in ascending order
+* **Space Complexity**: O(n) - Creates a copy of the input array
+* **Time Complexity**: 
+  - Average: O(n log n)
+  - Worst: O(n²) - rarely occurs due to randomization
+  - Best: O(n log n)
+
+##### 2. `randomized_partition(arr, low, high)`
+
+* **Purpose**: Partition array using a randomly selected pivot
+* **Parameters**: 
+  - `arr` (list) - Array to partition
+  - `low` (int) - Starting index
+  - `high` (int) - Ending index
+* **Returns**: `int` - Final position of pivot element
+* **Key Feature**: Random pivot selection prevents worst-case O(n²) performance
+
+##### 3. `compare_with_builtin(arr)`
+
+* **Purpose**: Compare randomized quicksort with Python's built-in sort
+* **Returns**: Dictionary with timing metrics and correctness verification
+
+##### 4. `analyze_performance(array_sizes)`
+
+* **Purpose**: Analyze quicksort performance across different array sizes
+* **Returns**: List of performance metrics for each array size
+
+#### Algorithm Logic
+
+**Why Randomization?**
+
+Standard quicksort can degrade to O(n²) when:
+- Pivot is always the smallest element (worst case)
+- Pivot is always the largest element (worst case)
+- Array is already sorted or reverse sorted
+
+Randomization ensures:
+- Expected O(n log n) performance
+- Expected number of comparisons: 2n ln n ≈ 1.39n log₂ n
+- Very low probability of worst-case behavior
+
+### Part 2: Hash Table with Chaining
+
+#### Core Operations
+
+##### 1. `insert(key, value)`
+
+* **Purpose**: Insert or update a key-value pair
+* **Time Complexity**: O(1) average case, O(n) worst case
+* **Features**: 
+  - Automatically updates if key exists
+  - Triggers resize when load factor exceeds threshold
+
+##### 2. `get(key)`
+
+* **Purpose**: Retrieve value associated with a key
+* **Time Complexity**: O(1) average case, O(n) worst case
+* **Returns**: Value if key exists, None otherwise
+
+##### 3. `delete(key)`
+
+* **Purpose**: Remove a key-value pair
+* **Time Complexity**: O(1) average case, O(n) worst case
+* **Returns**: True if key was found and deleted, False otherwise
+
+##### 4. `contains(key)`
+
+* **Purpose**: Check if a key exists in the hash table
+* **Time Complexity**: O(1) average case, O(n) worst case
+* **Pythonic**: Supports `in` operator
+
+#### Hash Function
+
+**Multiplication Method:**
+```
+h(k) = floor(m × (k × A mod 1))
+```
+where:
+- `m` = table size
+- `A` ≈ (√5 - 1) / 2 ≈ 0.618 (golden ratio)
+- Provides good distribution of keys across buckets
+
+#### Collision Resolution
+
+**Chaining Strategy:**
+- Each bucket contains a linked list of key-value pairs
+- When collision occurs, new element is appended to chain
+- Allows multiple elements per bucket
+- No clustering issues unlike open addressing
+
+#### Dynamic Resizing
+
+**Load Factor Management:**
+- Default threshold: 0.75
+- When load factor exceeds threshold, table size doubles
+- All elements are rehashed into new table
+- Maintains O(1) average performance
+
+## Complexity Analysis
+
+### Randomized Quicksort
+
+| Aspect               | Complexity | Description                                        |
+| -------------------- | ---------- | -------------------------------------------------- |
+| **Time Complexity**  | O(n log n) | Average case - randomized pivot selection          |
+| **Worst Case**       | O(n²)      | Rarely occurs due to randomization                 |
+| **Best Case**        | O(n log n) | Already sorted arrays                              |
+| **Space Complexity** | O(log n)   | Average case recursion stack depth                 |
+| **Stability**        | Not Stable | Equal elements may change relative order           |
+
+### Hash Table with Chaining
+
+| Aspect               | Complexity | Description                                        |
+| -------------------- | ---------- | -------------------------------------------------- |
+| **Time Complexity**  | O(1)       | Average case for insert, get, delete               |
+| **Worst Case**       | O(n)       | All keys hash to same bucket (rare)                |
+| **Space Complexity** | O(n + m)   | n elements + m buckets                             |
+| **Load Factor**      | 0.75       | Threshold for automatic resizing                   |
+
+## Usage Examples
+
+### Basic Usage - Randomized Quicksort
+
+```python
+from src.quicksort import randomized_quicksort, compare_with_builtin
+
+# Example 1: Basic sorting
+arr = [64, 34, 25, 12, 22, 11, 90, 5]
+sorted_arr = randomized_quicksort(arr)
+print(sorted_arr)  # Output: [5, 11, 12, 22, 25, 34, 64, 90]
+
+# Example 2: Performance comparison
+comparison = compare_with_builtin(arr)
+print(f"Quicksort time: {comparison['quicksort_time']:.6f} seconds")
+print(f"Built-in sort time: {comparison['builtin_time']:.6f} seconds")
+print(f"Speedup ratio: {comparison['speedup']:.2f}x")
+print(f"Results match: {comparison['is_correct']}")
+```
+
+### Basic Usage - Hash Table
+
+```python
+from src.hash_table import HashTable
+
+# Create hash table
+ht = HashTable(initial_size=16)
+
+# Insert key-value pairs
+ht.insert(1, "apple")
+ht.insert(2, "banana")
+ht.insert(3, "cherry")
+
+# Retrieve values
+print(ht.get(1))  # "apple"
+
+# Check if key exists
+print(2 in ht)  # True
+
+# Delete a key
+ht.delete(2)
+
+# Get all items
+items = ht.get_all_items()
+print(items)  # [(1, "apple"), (3, "cherry")]
+```
+
+### Edge Cases Handled
+
+#### Quicksort
+
+```python
+# Empty array
+empty_arr = []
+result = randomized_quicksort(empty_arr)
+print(result)  # Output: []
+
+# Single element
+single = [42]
+result = randomized_quicksort(single)
+print(result)  # Output: [42]
+
+# Duplicate elements
+duplicates = [3, 3, 3, 3]
+result = randomized_quicksort(duplicates)
+print(result)  # Output: [3, 3, 3, 3]
+
+# Negative numbers
+negatives = [-5, -2, -8, 1, 3, -1, 0]
+result = randomized_quicksort(negatives)
+print(result)  # Output: [-8, -5, -2, -1, 0, 1, 3]
+```
+
+#### Hash Table
+
+```python
+# Empty hash table
+ht = HashTable()
+print(len(ht))  # 0
+print(ht.get(1))  # None
+
+# Collision handling
+ht = HashTable(initial_size=5)
+ht.insert(1, "one")
+ht.insert(6, "six")  # May collide with 1
+ht.insert(11, "eleven")  # May collide with 1 and 6
+# All keys are stored correctly via chaining
+
+# Load factor management
+ht = HashTable(initial_size=4, load_factor_threshold=0.75)
+ht.insert(1, "a")
+ht.insert(2, "b")
+ht.insert(3, "c")
+ht.insert(4, "d")  # Triggers resize (load factor = 1.0 > 0.75)
+print(ht.size)  # 8 (doubled)
+```
+
+## Running the Program
+
+### Prerequisites
+
+* Python 3.7 or higher
+* No external dependencies required (uses only Python standard library)
+
+### Execution
+
+#### Run Examples
+
+```bash
+python3 -m src.examples
+```
+
+#### Run Tests
+
+**Quick Tests (Essential functionality):**
+```bash
+python3 run_tests.py --quick
+```
+
+**Full Test Suite:**
+```bash
+python3 run_tests.py
+```
+
+**Unit Tests Only:**
+```bash
+python3 run_tests.py --unit-only
+```
+
+**Performance Benchmarks:**
+```bash
+python3 run_tests.py --benchmark
+```
+
+**Stress Tests:**
+```bash
+python3 run_tests.py --stress
+```
+
+**Negative Test Cases:**
+```bash
+python3 run_tests.py --negative
+```
+
+**Using unittest directly:**
+```bash
+python3 -m unittest discover tests -v
+```
+
+## Test Cases
+
+### Randomized Quicksort Tests
+
+The test suite includes comprehensive test cases covering:
+
+#### ✅ **Functional Tests**
+
+* Basic sorting functionality
+* Already sorted arrays (ascending/descending)
+* Empty arrays and single elements
+* Duplicate elements
+* Negative numbers and zero values
+* Large arrays (1000+ elements)
+
+#### ✅ **Behavioral Tests**
+
+* Non-destructive sorting (original array unchanged)
+* Correctness verification against built-in sort
+* Partition function correctness
+
+#### ✅ **Performance Tests**
+
+* Comparison with built-in sort
+* Performance analysis across different array sizes
+* Timing measurements
+
+### Hash Table Tests
+
+The test suite includes comprehensive test cases covering:
+
+#### ✅ **Functional Tests**
+
+* Basic insert, get, delete operations
+* Empty hash table operations
+* Collision handling
+* Load factor calculation
+* Dynamic resizing
+
+#### ✅ **Behavioral Tests**
+
+* Key existence checking (`in` operator)
+* Update existing keys
+* Delete from chains (middle of chain)
+* Get all items
+
+#### ✅ **Edge Cases**
+
+* Empty hash table
+* Single element
+* All keys hash to same bucket
+* Load factor threshold triggering resize
+
+## Project Structure
+
+```
+MSCS532_Assignment3/
+├── src/
+│   ├── __init__.py                # Package initialization
+│   ├── quicksort.py              # Randomized Quicksort implementation
+│   ├── hash_table.py             # Hash Table with Chaining implementation
+│   └── examples.py               # Example usage demonstrations
+├── tests/
+│   ├── __init__.py               # Test package initialization
+│   ├── test_quicksort.py         # Comprehensive quicksort tests
+│   └── test_hash_table.py        # Comprehensive hash table tests
+├── run_tests.py                  # Test runner with various options
+├── README.md                     # This documentation
+├── LICENSE                       # MIT License
+├── .gitignore                    # Git ignore file
+└── requirements.txt              # Python dependencies (none required)
+```
+
+## Testing
+
+### Test Coverage
+
+The project includes **30+ comprehensive test cases** covering:
+
+#### ✅ **Functional Tests**
+
+* Basic functionality for both algorithms
+* Edge cases (empty, single element, duplicates)
+* Correctness verification
+
+#### ✅ **Behavioral Tests**
+
+* Non-destructive operations
+* In-place modifications
+* Collision resolution
+* Dynamic resizing
+
+#### ✅ **Performance Tests**
+
+* Timing comparisons
+* Performance analysis across different sizes
+* Benchmarking utilities
+
+#### ✅ **Stress Tests**
+
+* Large arrays (1000+ elements)
+* Many hash table operations
+* Boundary conditions
+
+#### ✅ **Negative Test Cases**
+
+* Invalid input types
+* Edge cases and boundary conditions
+* Error handling
+
+### Running Tests
+
+The project includes a comprehensive test runner (`run_tests.py`) with multiple options:
+
+- **Quick Tests**: Essential functionality tests
+- **Full Suite**: All tests including edge cases
+- **Unit Tests**: Standard unittest tests only
+- **Benchmarks**: Performance comparison tests
+- **Stress Tests**: Large-scale and boundary tests
+- **Negative Tests**: Invalid input and error handling tests
+
+## Educational Value
+
+This implementation serves as an excellent learning resource for:
+
+* **Algorithm Understanding**: Clear demonstration of quicksort and hash table mechanics
+* **Randomization Techniques**: Shows how randomization improves algorithm performance
+* **Data Structure Design**: Demonstrates hash table implementation with collision resolution
+* **Code Quality**: Demonstrates good practices in Python programming
+* **Testing**: Comprehensive test suite showing edge case handling
+* **Documentation**: Well-commented code with clear explanations
+* **Performance Analysis**: Tools for understanding algorithm efficiency
+
+## Algorithm Analysis
+
+### Randomized Quicksort
+
+**Why Randomization?**
+- Standard quicksort can degrade to O(n²) when the pivot is always the smallest or largest element
+- Randomization ensures expected O(n log n) performance
+- Expected number of comparisons: 2n ln n ≈ 1.39n log₂ n
+
+**Performance Characteristics:**
+- Excellent average-case performance
+- Non-destructive sorting (creates copy)
+- Cache-friendly due to good locality of reference
+
+**Comparison with Other Algorithms:**
+- Faster than O(n²) algorithms (bubble, insertion, selection sort)
+- Comparable to merge sort but with better space efficiency
+- Generally slower than Python's built-in Timsort (optimized hybrid)
+
+### Hash Table with Chaining
+
+**Chaining vs. Open Addressing:**
+- Chaining stores multiple elements in the same bucket using linked lists
+- Handles collisions gracefully without clustering
+- Load factor threshold prevents performance degradation
+
+**Hash Function:**
+- Uses multiplication method: h(k) = floor(m × (k × A mod 1))
+- A ≈ (√5 - 1) / 2 ≈ 0.618 (golden ratio)
+- Provides good distribution of keys across buckets
+
+**Performance Considerations:**
+- O(1) average case performance
+- Dynamic resizing maintains efficiency
+- Trade-off between space and time efficiency
+
+## Performance Considerations
+
+1. **Quicksort**: 
+   - Best for general-purpose sorting
+   - Randomization prevents worst-case scenarios
+   - Good for medium to large arrays
+
+2. **Hash Table**: 
+   - Maintains O(1) average performance through load factor management
+   - Resizing doubles table size when threshold is exceeded
+   - Trade-off between space and time efficiency
+
+## Contributing
+
+This is an educational project demonstrating algorithm implementations. Feel free to:
+
+* Add more test cases
+* Implement additional algorithms
+* Improve documentation
+* Optimize the implementations
+* Add visualization tools
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Author
+
+Created for MSCS532 Assignment 3: Understanding Algorithm Efficiency and Scalability
+
+## Acknowledgments
+
+* Based on standard algorithm implementations from Introduction to Algorithms (CLRS)
+* Educational project for algorithm analysis and data structures course

requirements.txt

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+# Requirements for MSCS532 Assignment 3
+# No external dependencies required - uses only Python standard library
+

run_tests.py

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+#!/usr/bin/env python3
+"""
+Test runner for MSCS532 Assignment 3.
+
+Provides various test execution options:
+- Quick tests: Essential functionality
+- Full suite: All tests including edge cases
+- Unit tests: Standard unittest tests only
+- Benchmarks: Performance comparison tests
+- Stress tests: Large-scale and boundary tests
+- Negative tests: Invalid input and error handling
+"""
+
+import unittest
+import sys
+import argparse
+import time
+from typing import List, Dict
+
+
+def run_quick_tests():
+    """Run essential functionality tests."""
+    print("=" * 70)
+    print("Running Quick Tests (Essential Functionality)")
+    print("=" * 70)
+    
+    loader = unittest.TestLoader()
+    suite = unittest.TestSuite()
+    
+    # Add basic functional tests
+    from tests.test_quicksort import (
+        TestRandomizedQuicksort,
+        TestPartition
+    )
+    from tests.test_hash_table import TestHashTable
+    
+    suite.addTests(loader.loadTestsFromTestCase(TestRandomizedQuicksort))
+    suite.addTests(loader.loadTestsFromTestCase(TestPartition))
+    suite.addTests(loader.loadTestsFromTestCase(TestHashTable))
+    
+    runner = unittest.TextTestRunner(verbosity=2)
+    result = runner.run(suite)
+    return result.wasSuccessful()
+
+
+def run_unit_tests():
+    """Run standard unittest tests."""
+    print("=" * 70)
+    print("Running Unit Tests")
+    print("=" * 70)
+    
+    loader = unittest.TestLoader()
+    suite = loader.discover('tests', pattern='test_*.py')
+    
+    runner = unittest.TextTestRunner(verbosity=2)
+    result = runner.run(suite)
+    return result.wasSuccessful()
+
+
+def run_performance_tests():
+    """Run performance benchmark tests."""
+    print("=" * 70)
+    print("Running Performance Benchmarks")
+    print("=" * 70)
+    
+    try:
+        from src.quicksort import analyze_performance, compare_with_builtin
+        import random
+        
+        print("\n1. Quicksort Performance Analysis:")
+        print("-" * 70)
+        sizes = [100, 1000, 10000]
+        results = analyze_performance(sizes)
+        
+        print(f"\n{'Size':<10} {'Quicksort Time':<18} {'Built-in Time':<18} {'Speedup':<10} {'Correct':<10}")
+        print("-" * 70)
+        for result in results:
+            print(f"{result['array_length']:<10} "
+                  f"{result['quicksort_time']:<18.6f} "
+                  f"{result['builtin_time']:<18.6f} "
+                  f"{result['speedup']:<10.2f} "
+                  f"{str(result['is_correct']):<10}")
+        
+        print("\n2. Hash Table Performance:")
+        print("-" * 70)
+        from src.hash_table import HashTable
+        
+        ht = HashTable(initial_size=16)
+        num_operations = 10000
+        
+        start_time = time.perf_counter()
+        for i in range(num_operations):
+            ht.insert(i, f"value_{i}")
+        insert_time = time.perf_counter() - start_time
+        
+        start_time = time.perf_counter()
+        for i in range(num_operations):
+            _ = ht.get(i)
+        get_time = time.perf_counter() - start_time
+        
+        start_time = time.perf_counter()
+        for i in range(num_operations):
+            ht.delete(i)
+        delete_time = time.perf_counter() - start_time
+        
+        print(f"Insert {num_operations} elements: {insert_time:.6f} seconds")
+        print(f"Get {num_operations} elements: {get_time:.6f} seconds")
+        print(f"Delete {num_operations} elements: {delete_time:.6f} seconds")
+        print(f"Average insert time: {insert_time/num_operations*1000:.4f} ms")
+        print(f"Average get time: {get_time/num_operations*1000:.4f} ms")
+        print(f"Average delete time: {delete_time/num_operations*1000:.4f} ms")
+        
+        return True
+        
+    except Exception as e:
+        print(f"Error running performance tests: {e}")
+        return False
+
+
+def run_stress_tests():
+    """Run stress tests with large inputs."""
+    print("=" * 70)
+    print("Running Stress Tests")
+    print("=" * 70)
+    
+    try:
+        from src.quicksort import randomized_quicksort
+        from src.hash_table import HashTable
+        import random
+        
+        print("\n1. Quicksort Stress Tests:")
+        print("-" * 70)
+        
+        # Test with very large array
+        large_size = 50000
+        print(f"Testing with array of size {large_size}...")
+        large_arr = [random.randint(1, 1000000) for _ in range(large_size)]
+        
+        start_time = time.perf_counter()
+        sorted_arr = randomized_quicksort(large_arr)
+        elapsed = time.perf_counter() - start_time
+        
+        # Verify correctness
+        is_correct = sorted_arr == sorted(large_arr)
+        print(f"✓ Large array sorted in {elapsed:.4f} seconds")
+        print(f"✓ Correctness: {is_correct}")
+        
+        # Test with worst-case scenario (many duplicates)
+        print(f"\nTesting with array of size {large_size} (many duplicates)...")
+        dup_arr = [random.randint(1, 100) for _ in range(large_size)]
+        
+        start_time = time.perf_counter()
+        sorted_dup = randomized_quicksort(dup_arr)
+        elapsed = time.perf_counter() - start_time
+        
+        is_correct = sorted_dup == sorted(dup_arr)
+        print(f"✓ Duplicate-heavy array sorted in {elapsed:.4f} seconds")
+        print(f"✓ Correctness: {is_correct}")
+        
+        print("\n2. Hash Table Stress Tests:")
+        print("-" * 70)
+        
+        # Test with many insertions
+        ht = HashTable(initial_size=16)
+        num_inserts = 100000
+        
+        print(f"Inserting {num_inserts} elements...")
+        start_time = time.perf_counter()
+        for i in range(num_inserts):
+            ht.insert(i, f"value_{i}")
+        elapsed = time.perf_counter() - start_time
+        
+        print(f"✓ Inserted {num_inserts} elements in {elapsed:.4f} seconds")
+        print(f"✓ Hash table size: {ht.size}")
+        print(f"✓ Load factor: {ht.get_load_factor():.4f}")
+        print(f"✓ Count: {len(ht)}")
+        
+        # Verify all elements are retrievable
+        print(f"\nVerifying retrieval of {num_inserts} elements...")
+        start_time = time.perf_counter()
+        all_found = True
+        for i in range(num_inserts):
+            if ht.get(i) != f"value_{i}":
+                all_found = False
+                break
+        elapsed = time.perf_counter() - start_time
+        
+        print(f"✓ Retrieved {num_inserts} elements in {elapsed:.4f} seconds")
+        print(f"✓ All elements found: {all_found}")
+        
+        return True
+        
+    except Exception as e:
+        print(f"Error running stress tests: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def run_negative_tests():
+    """Run negative test cases (invalid inputs, error handling)."""
+    print("=" * 70)
+    print("Running Negative Test Cases")
+    print("=" * 70)
+    
+    try:
+        from src.quicksort import randomized_quicksort
+        from src.hash_table import HashTable
+        
+        print("\n1. Quicksort Negative Tests:")
+        print("-" * 70)
+        
+        # Test with None (should handle gracefully or raise appropriate error)
+        try:
+            result = randomized_quicksort(None)
+            print("✗ Should have raised TypeError for None input")
+        except (TypeError, AttributeError):
+            print("✓ Correctly handles None input")
+        
+        # Test with mixed types (should raise TypeError)
+        try:
+            result = randomized_quicksort([1, 2, "three", 4])
+            print("✗ Should have raised TypeError for mixed types")
+        except TypeError:
+            print("✓ Correctly raises TypeError for mixed types")
+        
+        print("\n2. Hash Table Negative Tests:")
+        print("-" * 70)
+        
+        # Test with None key
+        ht = HashTable()
+        try:
+            ht.insert(None, "value")
+            print("✗ Should have raised TypeError for None key")
+        except TypeError:
+            print("✓ Correctly handles None key")
+        
+        # Test with invalid initial size
+        try:
+            ht = HashTable(initial_size=0)
+            print("✗ Should handle invalid initial size")
+        except (ValueError, ZeroDivisionError):
+            print("✓ Correctly handles invalid initial size")
+        
+        # Test with negative initial size
+        try:
+            ht = HashTable(initial_size=-1)
+            print("✗ Should handle negative initial size")
+        except (ValueError, AssertionError):
+            print("✓ Correctly handles negative initial size")
+        
+        print("\n✓ Negative tests completed")
+        return True
+        
+    except Exception as e:
+        print(f"Error running negative tests: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def run_full_suite():
+    """Run all tests."""
+    print("=" * 70)
+    print("Running Full Test Suite")
+    print("=" * 70)
+    
+    results = []
+    
+    print("\n[1/4] Running Unit Tests...")
+    results.append(("Unit Tests", run_unit_tests()))
+    
+    print("\n[2/4] Running Performance Benchmarks...")
+    results.append(("Performance Tests", run_performance_tests()))
+    
+    print("\n[3/4] Running Stress Tests...")
+    results.append(("Stress Tests", run_stress_tests()))
+    
+    print("\n[4/4] Running Negative Tests...")
+    results.append(("Negative Tests", run_negative_tests()))
+    
+    print("\n" + "=" * 70)
+    print("Test Summary")
+    print("=" * 70)
+    
+    for test_name, success in results:
+        status = "✓ PASSED" if success else "✗ FAILED"
+        print(f"{test_name:<25} {status}")
+    
+    all_passed = all(result[1] for result in results)
+    return all_passed
+
+
+def main():
+    """Main test runner with command-line interface."""
+    parser = argparse.ArgumentParser(
+        description='Test runner for MSCS532 Assignment 3',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python3 run_tests.py --quick          # Run quick tests
+  python3 run_tests.py                  # Run full suite
+  python3 run_tests.py --unit-only      # Run unit tests only
+  python3 run_tests.py --benchmark      # Run performance benchmarks
+  python3 run_tests.py --stress         # Run stress tests
+  python3 run_tests.py --negative       # Run negative test cases
+        """
+    )
+    
+    parser.add_argument(
+        '--quick',
+        action='store_true',
+        help='Run quick tests (essential functionality only)'
+    )
+    parser.add_argument(
+        '--unit-only',
+        action='store_true',
+        help='Run unit tests only'
+    )
+    parser.add_argument(
+        '--benchmark',
+        action='store_true',
+        help='Run performance benchmarks'
+    )
+    parser.add_argument(
+        '--stress',
+        action='store_true',
+        help='Run stress tests'
+    )
+    parser.add_argument(
+        '--negative',
+        action='store_true',
+        help='Run negative test cases'
+    )
+    
+    args = parser.parse_args()
+    
+    success = False
+    
+    if args.quick:
+        success = run_quick_tests()
+    elif args.unit_only:
+        success = run_unit_tests()
+    elif args.benchmark:
+        success = run_performance_tests()
+    elif args.stress:
+        success = run_stress_tests()
+    elif args.negative:
+        success = run_negative_tests()
+    else:
+        # Default: run full suite
+        success = run_full_suite()
+    
+    sys.exit(0 if success else 1)
+
+
+if __name__ == '__main__':
+    main()
+

src/__init__.py

@@ -0,0 +1,10 @@
+"""
+MSCS532 Assignment 3: Understanding Algorithm Efficiency and Scalability
+
+This package contains implementations of:
+- Randomized Quicksort algorithm
+- Hashing with Chaining data structure
+"""
+
+__version__ = "1.0.0"
+

src/examples.py

@@ -0,0 +1,148 @@
+"""
+Example usage of Randomized Quicksort and Hash Table implementations.
+
+This module demonstrates how to use the algorithms and data structures
+implemented in this project.
+"""
+
+import random
+from src.quicksort import (
+    randomized_quicksort,
+    compare_with_builtin,
+    analyze_performance
+)
+from src.hash_table import HashTable
+
+
+def example_quicksort():
+    """Demonstrate randomized quicksort usage."""
+    print("=" * 60)
+    print("Randomized Quicksort Example")
+    print("=" * 60)
+    
+    # Example 1: Basic sorting
+    print("\n1. Basic Sorting:")
+    arr = [64, 34, 25, 12, 22, 11, 90, 5]
+    print(f"Original array: {arr}")
+    sorted_arr = randomized_quicksort(arr)
+    print(f"Sorted array: {sorted_arr}")
+    
+    # Example 2: Large random array
+    print("\n2. Large Random Array:")
+    large_arr = [random.randint(1, 1000) for _ in range(20)]
+    print(f"Original array (first 20 elements): {large_arr[:20]}")
+    sorted_large = randomized_quicksort(large_arr)
+    print(f"Sorted array (first 20 elements): {sorted_large[:20]}")
+    
+    # Example 3: Performance comparison
+    print("\n3. Performance Comparison with Built-in Sort:")
+    test_array = [random.randint(1, 100000) for _ in range(10000)]
+    comparison = compare_with_builtin(test_array)
+    print(f"Array length: {comparison['array_length']}")
+    print(f"Quicksort time: {comparison['quicksort_time']:.6f} seconds")
+    print(f"Built-in sort time: {comparison['builtin_time']:.6f} seconds")
+    print(f"Speedup ratio: {comparison['speedup']:.2f}x")
+    print(f"Results match: {comparison['is_correct']}")
+
+
+def example_hash_table():
+    """Demonstrate hash table with chaining usage."""
+    print("\n" + "=" * 60)
+    print("Hash Table with Chaining Example")
+    print("=" * 60)
+    
+    # Create hash table
+    ht = HashTable(initial_size=8)
+    
+    # Example 1: Insert operations
+    print("\n1. Insert Operations:")
+    keys_values = [
+        (1, "apple"),
+        (2, "banana"),
+        (3, "cherry"),
+        (10, "date"),
+        (11, "elderberry"),
+        (18, "fig"),
+        (19, "grape"),
+        (26, "honeydew")
+    ]
+    
+    for key, value in keys_values:
+        ht.insert(key, value)
+        print(f"Inserted ({key}, {value}) - Load factor: {ht.get_load_factor():.2f}")
+    
+    print(f"\nHash table size: {ht.size}")
+    print(f"Number of elements: {len(ht)}")
+    print(f"Load factor: {ht.get_load_factor():.2f}")
+    
+    # Example 2: Search operations
+    print("\n2. Search Operations:")
+    search_keys = [1, 3, 11, 99]
+    for key in search_keys:
+        value = ht.get(key)
+        if value:
+            print(f"Key {key} found: {value}")
+        else:
+            print(f"Key {key} not found")
+    
+    # Example 3: Contains operator
+    print("\n3. Using 'in' Operator:")
+    test_keys = [2, 5, 18]
+    for key in test_keys:
+        print(f"{key} in hash table: {key in ht}")
+    
+    # Example 4: Delete operations
+    print("\n4. Delete Operations:")
+    delete_key = 3
+    print(f"Deleting key {delete_key}...")
+    deleted = ht.delete(delete_key)
+    print(f"Delete successful: {deleted}")
+    print(f"Key {delete_key} still exists: {delete_key in ht}")
+    print(f"Updated count: {len(ht)}")
+    
+    # Example 5: Get all items
+    print("\n5. All Items in Hash Table:")
+    all_items = ht.get_all_items()
+    for key, value in all_items:
+        print(f"  Key: {key}, Value: {value}")
+    
+    # Example 6: Collision demonstration
+    print("\n6. Collision Resolution (Chaining):")
+    collision_ht = HashTable(initial_size=5)
+    # Keys that will likely collide
+    collision_keys = [1, 6, 11, 16, 21]
+    for key in collision_keys:
+        collision_ht.insert(key, f"value_{key}")
+    print(f"Hash table with collisions:")
+    print(f"  Size: {collision_ht.size}")
+    print(f"  Count: {len(collision_ht)}")
+    print(f"  Load factor: {collision_ht.get_load_factor():.2f}")
+    print(f"  Items: {collision_ht.get_all_items()}")
+
+
+def example_performance_analysis():
+    """Demonstrate performance analysis of quicksort."""
+    print("\n" + "=" * 60)
+    print("Performance Analysis Example")
+    print("=" * 60)
+    
+    print("\nAnalyzing quicksort performance across different array sizes:")
+    results = analyze_performance([100, 1000, 10000])
+    
+    print("\nResults:")
+    print(f"{'Size':<10} {'Quicksort Time':<18} {'Built-in Time':<18} {'Speedup':<10} {'Correct':<10}")
+    print("-" * 70)
+    for result in results:
+        print(f"{result['array_length']:<10} "
+              f"{result['quicksort_time']:<18.6f} "
+              f"{result['builtin_time']:<18.6f} "
+              f"{result['speedup']:<10.2f} "
+              f"{str(result['is_correct']):<10}")
+
+
+if __name__ == "__main__":
+    # Run all examples
+    example_quicksort()
+    example_hash_table()
+    example_performance_analysis()
+

src/hash_table.py

@@ -0,0 +1,204 @@
+"""
+Hash Table with Chaining Implementation
+
+This module provides a hash table implementation using chaining
+for collision resolution.
+"""
+
+from typing import List, Optional, Tuple, Iterator
+from dataclasses import dataclass
+
+
+@dataclass
+class HashNode:
+    """Node for storing key-value pairs in hash table chains."""
+    key: int
+    value: any
+    next: Optional['HashNode'] = None
+
+
+class HashTable:
+    """
+    Hash Table implementation using chaining for collision resolution.
+    
+    Chaining stores multiple elements in the same bucket using a linked list.
+    When a collision occurs, the new element is appended to the chain.
+    
+    Time Complexity:
+        - Average: O(1) for insert, search, delete
+        - Worst: O(n) when all keys hash to the same bucket
+    
+    Space Complexity: O(n + m) where n is number of elements, m is table size
+    """
+    
+    def __init__(self, initial_size: int = 16, load_factor_threshold: float = 0.75):
+        """
+        Initialize hash table.
+        
+        Args:
+            initial_size: Initial size of the hash table
+            load_factor_threshold: Threshold for resizing (default: 0.75)
+        """
+        self.size = initial_size
+        self.load_factor_threshold = load_factor_threshold
+        self.count = 0
+        self.buckets: List[Optional[HashNode]] = [None] * self.size
+    
+    def _hash(self, key: int) -> int:
+        """
+        Hash function using multiplication method.
+        
+        Args:
+            key: Key to hash
+        
+        Returns:
+            Hash value (bucket index)
+        """
+        # Using multiplication method: h(k) = floor(m * (k * A mod 1))
+        # where A ≈ (√5 - 1) / 2 ≈ 0.618
+        A = 0.6180339887498949
+        return int(self.size * ((key * A) % 1))
+    
+    def _resize(self) -> None:
+        """Resize hash table when load factor exceeds threshold."""
+        old_buckets = self.buckets
+        old_size = self.size
+        
+        # Double the size
+        self.size *= 2
+        self.count = 0
+        self.buckets = [None] * self.size
+        
+        # Rehash all existing elements
+        for bucket in old_buckets:
+            current = bucket
+            while current is not None:
+                self.insert(current.key, current.value)
+                current = current.next
+    
+    def insert(self, key: int, value: any) -> None:
+        """
+        Insert a key-value pair into the hash table.
+        
+        Args:
+            key: Key to insert
+            value: Value associated with the key
+        """
+        # Check if resize is needed
+        load_factor = self.count / self.size
+        if load_factor >= self.load_factor_threshold:
+            self._resize()
+        
+        bucket_index = self._hash(key)
+        
+        # Check if key already exists
+        current = self.buckets[bucket_index]
+        while current is not None:
+            if current.key == key:
+                current.value = value  # Update existing key
+                return
+            current = current.next
+        
+        # Insert new node at the beginning of the chain
+        new_node = HashNode(key, value, self.buckets[bucket_index])
+        self.buckets[bucket_index] = new_node
+        self.count += 1
+    
+    def get(self, key: int) -> Optional[any]:
+        """
+        Retrieve value associated with a key.
+        
+        Args:
+            key: Key to search for
+        
+        Returns:
+            Value associated with key, or None if not found
+        """
+        bucket_index = self._hash(key)
+        current = self.buckets[bucket_index]
+        
+        while current is not None:
+            if current.key == key:
+                return current.value
+            current = current.next
+        
+        return None
+    
+    def delete(self, key: int) -> bool:
+        """
+        Delete a key-value pair from the hash table.
+        
+        Args:
+            key: Key to delete
+        
+        Returns:
+            True if key was found and deleted, False otherwise
+        """
+        bucket_index = self._hash(key)
+        current = self.buckets[bucket_index]
+        prev = None
+        
+        while current is not None:
+            if current.key == key:
+                if prev is None:
+                    # Node to delete is at the head of chain
+                    self.buckets[bucket_index] = current.next
+                else:
+                    # Node to delete is in the middle or end
+                    prev.next = current.next
+                self.count -= 1
+                return True
+            prev = current
+            current = current.next
+        
+        return False
+    
+    def contains(self, key: int) -> bool:
+        """
+        Check if a key exists in the hash table.
+        
+        Args:
+            key: Key to check
+        
+        Returns:
+            True if key exists, False otherwise
+        """
+        return self.get(key) is not None
+    
+    def get_load_factor(self) -> float:
+        """
+        Get current load factor of the hash table.
+        
+        Returns:
+            Load factor (count / size)
+        """
+        return self.count / self.size if self.size > 0 else 0.0
+    
+    def get_all_items(self) -> List[Tuple[int, any]]:
+        """
+        Get all key-value pairs in the hash table.
+        
+        Returns:
+            List of (key, value) tuples
+        """
+        items = []
+        for bucket in self.buckets:
+            current = bucket
+            while current is not None:
+                items.append((current.key, current.value))
+                current = current.next
+        return items
+    
+    def __len__(self) -> int:
+        """Return the number of elements in the hash table."""
+        return self.count
+    
+    def __contains__(self, key: int) -> bool:
+        """Check if key exists in hash table using 'in' operator."""
+        return self.contains(key)
+    
+    def __repr__(self) -> str:
+        """String representation of the hash table."""
+        items = self.get_all_items()
+        return f"HashTable(size={self.size}, count={self.count}, load_factor={self.get_load_factor():.2f}, items={items})"
+

src/quicksort.py

@@ -0,0 +1,178 @@
+"""
+Randomized Quicksort Implementation
+
+This module provides a randomized quicksort algorithm implementation
+along with utilities for performance analysis and comparison.
+"""
+
+import random
+from typing import List, Callable, Tuple
+import time
+
+
+def randomized_quicksort(arr: List[int], low: int = None, high: int = None) -> List[int]:
+    """
+    Sort an array using randomized quicksort algorithm.
+    
+    Time Complexity:
+        - Average: O(n log n)
+        - Worst: O(n²) (rarely occurs due to randomization)
+        - Best: O(n log n)
+    
+    Space Complexity: O(log n) average case due to recursion stack
+    
+    Args:
+        arr: List of integers to sort
+        low: Starting index (default: 0)
+        high: Ending index (default: len(arr) - 1)
+    
+    Returns:
+        Sorted list of integers
+    """
+    if low is None:
+        low = 0
+    if high is None:
+        high = len(arr) - 1
+    
+    # Create a copy to avoid mutating the original array
+    arr = arr.copy()
+    
+    def _quicksort(arr: List[int], low: int, high: int) -> None:
+        """Internal recursive quicksort function."""
+        if low < high:
+            # Partition the array and get pivot index
+            pivot_idx = randomized_partition(arr, low, high)
+            
+            # Recursively sort elements before and after partition
+            _quicksort(arr, low, pivot_idx - 1)
+            _quicksort(arr, pivot_idx + 1, high)
+    
+    _quicksort(arr, low, high)
+    return arr
+
+
+def randomized_partition(arr: List[int], low: int, high: int) -> int:
+    """
+    Partition the array using a random pivot element.
+    
+    This randomization helps avoid worst-case O(n²) behavior
+    that occurs when the pivot is always the smallest or largest element.
+    
+    Args:
+        arr: List to partition
+        low: Starting index
+        high: Ending index
+    
+    Returns:
+        Final position of pivot element
+    """
+    # Randomly select a pivot index
+    random_idx = random.randint(low, high)
+    
+    # Swap random element with last element
+    arr[random_idx], arr[high] = arr[high], arr[random_idx]
+    
+    # Use standard partition with pivot at high
+    return partition(arr, low, high)
+
+
+def partition(arr: List[int], low: int, high: int) -> int:
+    """
+    Partition the array around a pivot element.
+    
+    Elements smaller than pivot go to the left,
+    elements greater than pivot go to the right.
+    
+    Args:
+        arr: List to partition
+        low: Starting index
+        high: Ending index (pivot position)
+    
+    Returns:
+        Final position of pivot element
+    """
+    pivot = arr[high]  # Pivot element
+    i = low - 1  # Index of smaller element
+    
+    for j in range(low, high):
+        # If current element is smaller than or equal to pivot
+        if arr[j] <= pivot:
+            i += 1
+            arr[i], arr[j] = arr[j], arr[i]
+    
+    # Place pivot in correct position
+    arr[i + 1], arr[high] = arr[high], arr[i + 1]
+    return i + 1
+
+
+def measure_time(func: Callable, *args, **kwargs) -> Tuple[float, any]:
+    """
+    Measure execution time of a function.
+    
+    Args:
+        func: Function to measure
+        *args: Positional arguments for function
+        **kwargs: Keyword arguments for function
+    
+    Returns:
+        Tuple of (execution_time_in_seconds, function_result)
+    """
+    start_time = time.perf_counter()
+    result = func(*args, **kwargs)
+    end_time = time.perf_counter()
+    return end_time - start_time, result
+
+
+def compare_with_builtin(arr: List[int]) -> dict:
+    """
+    Compare randomized quicksort with Python's built-in sort.
+    
+    Args:
+        arr: List to sort
+    
+    Returns:
+        Dictionary with timing comparison results
+    """
+    # Test randomized quicksort
+    quicksort_time, sorted_quicksort = measure_time(randomized_quicksort, arr)
+    
+    # Test built-in sort
+    builtin_time, sorted_builtin = measure_time(sorted, arr)
+    
+    # Verify correctness
+    is_correct = sorted_quicksort == sorted_builtin
+    
+    return {
+        'quicksort_time': quicksort_time,
+        'builtin_time': builtin_time,
+        'speedup': quicksort_time / builtin_time if builtin_time > 0 else float('inf'),
+        'is_correct': is_correct,
+        'array_length': len(arr)
+    }
+
+
+def analyze_performance(array_sizes: List[int] = None) -> List[dict]:
+    """
+    Analyze quicksort performance across different array sizes.
+    
+    Args:
+        array_sizes: List of array sizes to test (default: [100, 1000, 10000, 100000])
+    
+    Returns:
+        List of performance metrics for each array size
+    """
+    if array_sizes is None:
+        array_sizes = [100, 1000, 10000, 100000]
+    
+    results = []
+    
+    for size in array_sizes:
+        # Generate random array
+        test_array = [random.randint(1, 1000000) for _ in range(size)]
+        
+        # Measure performance
+        comparison = compare_with_builtin(test_array)
+        results.append(comparison)
+    
+    return results
+

tests/__init__.py

@@ -0,0 +1,4 @@
+"""
+Test suite for MSCS532 Assignment 3 implementations.
+"""
+

tests/test_hash_table.py

@@ -0,0 +1,218 @@
+"""
+Unit tests for Hash Table with Chaining implementation.
+"""
+
+import unittest
+from src.hash_table import HashTable, HashNode
+
+
+class TestHashTable(unittest.TestCase):
+    """Test cases for hash table with chaining."""
+    
+    def test_initialization(self):
+        """Test hash table initialization."""
+        ht = HashTable(initial_size=16)
+        self.assertEqual(ht.size, 16)
+        self.assertEqual(len(ht), 0)
+        self.assertEqual(ht.get_load_factor(), 0.0)
+    
+    def test_insert_and_get(self):
+        """Test basic insert and get operations."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        ht.insert(2, "banana")
+        
+        self.assertEqual(ht.get(1), "apple")
+        self.assertEqual(ht.get(2), "banana")
+        self.assertEqual(len(ht), 2)
+    
+    def test_insert_update(self):
+        """Test that inserting same key updates value."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        ht.insert(1, "banana")
+        
+        self.assertEqual(ht.get(1), "banana")
+        self.assertEqual(len(ht), 1)
+    
+    def test_get_nonexistent_key(self):
+        """Test getting a key that doesn't exist."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        
+        self.assertIsNone(ht.get(2))
+    
+    def test_delete_existing_key(self):
+        """Test deleting an existing key."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        ht.insert(2, "banana")
+        
+        deleted = ht.delete(1)
+        self.assertTrue(deleted)
+        self.assertIsNone(ht.get(1))
+        self.assertEqual(ht.get(2), "banana")
+        self.assertEqual(len(ht), 1)
+    
+    def test_delete_nonexistent_key(self):
+        """Test deleting a key that doesn't exist."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        
+        deleted = ht.delete(2)
+        self.assertFalse(deleted)
+        self.assertEqual(len(ht), 1)
+    
+    def test_contains(self):
+        """Test contains method."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        
+        self.assertTrue(ht.contains(1))
+        self.assertFalse(ht.contains(2))
+    
+    def test_in_operator(self):
+        """Test using 'in' operator."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        
+        self.assertIn(1, ht)
+        self.assertNotIn(2, ht)
+    
+    def test_load_factor(self):
+        """Test load factor calculation."""
+        ht = HashTable(initial_size=4)
+        
+        # Initially empty
+        self.assertEqual(ht.get_load_factor(), 0.0)
+        
+        # Add elements
+        ht.insert(1, "a")
+        self.assertEqual(ht.get_load_factor(), 0.25)
+        
+        ht.insert(2, "b")
+        self.assertEqual(ht.get_load_factor(), 0.5)
+        
+        ht.insert(3, "c")
+        self.assertEqual(ht.get_load_factor(), 0.75)
+    
+    def test_resize(self):
+        """Test automatic resizing when load factor threshold is reached."""
+        ht = HashTable(initial_size=4, load_factor_threshold=0.75)
+        
+        # Insert elements to trigger resize
+        ht.insert(1, "a")
+        ht.insert(2, "b")
+        ht.insert(3, "c")
+        # This should trigger resize (3/4 = 0.75)
+        ht.insert(4, "d")
+        
+        # Size should have doubled
+        self.assertEqual(ht.size, 8)
+        
+        # All elements should still be accessible
+        self.assertEqual(ht.get(1), "a")
+        self.assertEqual(ht.get(2), "b")
+        self.assertEqual(ht.get(3), "c")
+        self.assertEqual(ht.get(4), "d")
+        self.assertEqual(len(ht), 4)
+    
+    def test_get_all_items(self):
+        """Test getting all items from hash table."""
+        ht = HashTable()
+        ht.insert(1, "apple")
+        ht.insert(2, "banana")
+        ht.insert(3, "cherry")
+        
+        items = ht.get_all_items()
+        self.assertEqual(len(items), 3)
+        
+        # Check that all items are present
+        item_dict = dict(items)
+        self.assertEqual(item_dict[1], "apple")
+        self.assertEqual(item_dict[2], "banana")
+        self.assertEqual(item_dict[3], "cherry")
+    
+    def test_collision_handling(self):
+        """Test that collisions are handled correctly."""
+        ht = HashTable(initial_size=5)
+        
+        # Insert keys that might collide
+        keys = [1, 6, 11, 16, 21]
+        for key in keys:
+            ht.insert(key, f"value_{key}")
+        
+        # All keys should be retrievable
+        for key in keys:
+            self.assertEqual(ht.get(key), f"value_{key}")
+        
+        self.assertEqual(len(ht), len(keys))
+    
+    def test_delete_from_chain(self):
+        """Test deleting an element from the middle of a chain."""
+        ht = HashTable(initial_size=5)
+        
+        # Create a chain by inserting colliding keys
+        keys = [1, 6, 11]
+        for key in keys:
+            ht.insert(key, f"value_{key}")
+        
+        # Delete middle element
+        deleted = ht.delete(6)
+        self.assertTrue(deleted)
+        
+        # Remaining elements should still be accessible
+        self.assertEqual(ht.get(1), "value_1")
+        self.assertIsNone(ht.get(6))
+        self.assertEqual(ht.get(11), "value_11")
+        self.assertEqual(len(ht), 2)
+    
+    def test_len(self):
+        """Test __len__ method."""
+        ht = HashTable()
+        self.assertEqual(len(ht), 0)
+        
+        ht.insert(1, "a")
+        self.assertEqual(len(ht), 1)
+        
+        ht.insert(2, "b")
+        self.assertEqual(len(ht), 2)
+        
+        ht.delete(1)
+        self.assertEqual(len(ht), 1)
+    
+    def test_multiple_operations(self):
+        """Test a sequence of mixed operations."""
+        ht = HashTable()
+        
+        # Insert
+        ht.insert(1, "one")
+        ht.insert(2, "two")
+        ht.insert(3, "three")
+        
+        # Update
+        ht.insert(2, "TWO")
+        
+        # Delete
+        ht.delete(1)
+        
+        # Verify final state
+        self.assertIsNone(ht.get(1))
+        self.assertEqual(ht.get(2), "TWO")
+        self.assertEqual(ht.get(3), "three")
+        self.assertEqual(len(ht), 2)
+    
+    def test_empty_hash_table(self):
+        """Test operations on empty hash table."""
+        ht = HashTable()
+        
+        self.assertIsNone(ht.get(1))
+        self.assertFalse(ht.contains(1))
+        self.assertFalse(ht.delete(1))
+        self.assertEqual(ht.get_all_items(), [])
+        self.assertEqual(len(ht), 0)
+
+
+if __name__ == '__main__':
+    unittest.main()
+

tests/test_quicksort.py

@@ -0,0 +1,150 @@
+"""
+Unit tests for Randomized Quicksort implementation.
+"""
+
+import unittest
+import random
+from src.quicksort import (
+    randomized_quicksort,
+    partition,
+    randomized_partition,
+    compare_with_builtin,
+    analyze_performance
+)
+
+
+class TestRandomizedQuicksort(unittest.TestCase):
+    """Test cases for randomized quicksort algorithm."""
+    
+    def test_empty_array(self):
+        """Test sorting an empty array."""
+        arr = []
+        result = randomized_quicksort(arr)
+        self.assertEqual(result, [])
+    
+    def test_single_element(self):
+        """Test sorting an array with a single element."""
+        arr = [42]
+        result = randomized_quicksort(arr)
+        self.assertEqual(result, [42])
+    
+    def test_sorted_array(self):
+        """Test sorting an already sorted array."""
+        arr = [1, 2, 3, 4, 5]
+        result = randomized_quicksort(arr)
+        self.assertEqual(result, [1, 2, 3, 4, 5])
+    
+    def test_reverse_sorted_array(self):
+        """Test sorting a reverse sorted array."""
+        arr = [5, 4, 3, 2, 1]
+        result = randomized_quicksort(arr)
+        self.assertEqual(result, [1, 2, 3, 4, 5])
+    
+    def test_random_array(self):
+        """Test sorting a random array."""
+        arr = [64, 34, 25, 12, 22, 11, 90, 5]
+        result = randomized_quicksort(arr)
+        expected = sorted(arr)
+        self.assertEqual(result, expected)
+    
+    def test_duplicate_elements(self):
+        """Test sorting an array with duplicate elements."""
+        arr = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3]
+        result = randomized_quicksort(arr)
+        expected = sorted(arr)
+        self.assertEqual(result, expected)
+    
+    def test_negative_numbers(self):
+        """Test sorting an array with negative numbers."""
+        arr = [-5, -2, -8, 1, 3, -1, 0]
+        result = randomized_quicksort(arr)
+        expected = sorted(arr)
+        self.assertEqual(result, expected)
+    
+    def test_large_array(self):
+        """Test sorting a large array."""
+        arr = [random.randint(1, 10000) for _ in range(1000)]
+        result = randomized_quicksort(arr)
+        expected = sorted(arr)
+        self.assertEqual(result, expected)
+    
+    def test_original_array_not_modified(self):
+        """Test that the original array is not modified."""
+        arr = [64, 34, 25, 12, 22, 11, 90, 5]
+        original = arr.copy()
+        randomized_quicksort(arr)
+        self.assertEqual(arr, original)
+    
+    def test_all_same_elements(self):
+        """Test sorting an array with all same elements."""
+        arr = [5, 5, 5, 5, 5]
+        result = randomized_quicksort(arr)
+        self.assertEqual(result, [5, 5, 5, 5, 5])
+
+
+class TestPartition(unittest.TestCase):
+    """Test cases for partition function."""
+    
+    def test_partition(self):
+        """Test partition function."""
+        arr = [64, 34, 25, 12, 22, 11, 90, 5]
+        pivot_idx = partition(arr, 0, len(arr) - 1)
+        
+        # Check that pivot is in correct position
+        pivot_value = arr[pivot_idx]
+        # All elements before pivot should be <= pivot
+        for i in range(0, pivot_idx):
+            self.assertLessEqual(arr[i], pivot_value)
+        # All elements after pivot should be >= pivot
+        for i in range(pivot_idx + 1, len(arr)):
+            self.assertGreaterEqual(arr[i], pivot_value)
+    
+    def test_randomized_partition(self):
+        """Test randomized partition function."""
+        arr = [64, 34, 25, 12, 22, 11, 90, 5]
+        pivot_idx = randomized_partition(arr, 0, len(arr) - 1)
+        
+        # Check that pivot is in correct position
+        pivot_value = arr[pivot_idx]
+        # All elements before pivot should be <= pivot
+        for i in range(0, pivot_idx):
+            self.assertLessEqual(arr[i], pivot_value)
+        # All elements after pivot should be >= pivot
+        for i in range(pivot_idx + 1, len(arr)):
+            self.assertGreaterEqual(arr[i], pivot_value)
+
+
+class TestPerformanceComparison(unittest.TestCase):
+    """Test cases for performance comparison utilities."""
+    
+    def test_compare_with_builtin(self):
+        """Test comparison with built-in sort."""
+        arr = [random.randint(1, 1000) for _ in range(100)]
+        comparison = compare_with_builtin(arr)
+        
+        self.assertIn('quicksort_time', comparison)
+        self.assertIn('builtin_time', comparison)
+        self.assertIn('speedup', comparison)
+        self.assertIn('is_correct', comparison)
+        self.assertIn('array_length', comparison)
+        
+        self.assertTrue(comparison['is_correct'])
+        self.assertEqual(comparison['array_length'], 100)
+        self.assertGreater(comparison['quicksort_time'], 0)
+        self.assertGreater(comparison['builtin_time'], 0)
+    
+    def test_analyze_performance(self):
+        """Test performance analysis."""
+        results = analyze_performance([100, 1000])
+        
+        self.assertEqual(len(results), 2)
+        for result in results:
+            self.assertIn('quicksort_time', result)
+            self.assertIn('builtin_time', result)
+            self.assertIn('is_correct', result)
+            self.assertTrue(result['is_correct'])
+
+
+if __name__ == '__main__':
+    unittest.main()
+