This production-grade text analysis tool demonstrates mastery of dynamic programming, space optimization techniques, and algorithmic problem-solving. Built for comparing large text files, DNA sequences, version control diffs, and document similarity analysis.

• Object-Oriented Design: Encapsulated algorithm classes with single responsibility
• RAII Pattern: Automatic resource management for file I/O operations
• Space Optimization: Reduced O(n×m) to O(min(n,m)) for Edit Distance
• Command-Line Interface: Flexible argument parsing with validation
• Performance Monitoring: Built-in high-resolution timing using std::chrono

├── C++17 Standard
│   ├── STL Containers (vector, string)
│   ├── File I/O (fstream, sstream)
│   └── Chrono Library (high_resolution_clock)
├── Python 3.x
│   └── Test Data Generator
└── Dynamic Programming
    ├── Tabulation Method
    ├── Space Optimization
    └── Backtracking for Solution Construction

Problem: Given two sequences, find the longest subsequence present in both.

Approach:

• Classic 2D DP table construction
• Backtracking to reconstruct the actual subsequence
• Handles edge cases (identical strings, empty strings)

Complexity:

• Time: O(n × m) where n, m are string lengths
• Space: O(n × m) for DP table + O(min(n,m)) for result

Real-World Applications:

• Git diff algorithms
• Bioinformatics (DNA/protein sequence alignment)
• File comparison utilities
• Plagiarism detection systems

Problem: Minimum number of single-character edits (insertions, deletions, substitutions) to transform one string into another.

Approach:

• Space-optimized rolling array technique
• Reduces space from O(n×m) to O(min(n,m))
• Automatically swaps strings to use smaller as column dimension
• Pointer-based optimization to avoid string copying

Complexity:

• Time: O(n × m)
• Space: O(min(n, m)) ⚡ Optimized from O(n×m)

Real-World Applications:

• Spell checkers and autocorrect
• Fuzzy string matching
• DNA mutation analysis
• Natural Language Processing

Optimization Highlights:

// Space optimization: Only 2 rows instead of full matrix
vector<int> prev(m + 1, 0);
vector<int> curr(m + 1, 0);

// Pointer swap to minimize memory operations
if (str1->size() < str2->size()) {
    swap(str1, str2);
}

Problem: Find the shortest string that contains both input strings as subsequences.

Approach:

• LCS-based solution with intelligent character merging
• Backtracking with dual-pointer technique
• Handles remaining characters from both strings

Complexity:

• Time: O(n × m)
• Space: O(n × m) for DP table + O(n+m) for result

Real-World Applications:

• Data compression algorithms
• Version control merge operations
• String interleaving problems
• Bioinformatics sequence assembly

Tested on Intel Core i7 with 16GB RAM:

Edit Distance Optimization Impact:
┌─────────────────────────────────────┐
│ Traditional: O(n × m) = 400 MB      │
│ Optimized:   O(min(n,m)) = 80 KB    │
│ Reduction:   99.98% 🚀              │
└─────────────────────────────────────┘

• Linear Scalability: Handles files up to 50,000+ characters
• Memory Efficient: Edit Distance can process 100K+ char strings with <1MB RAM
• Production Ready: Robust error handling and input validation

g++ -std=c++17 -O3 -o text_analyzer main.cpp

# Compare two files using LCS
./text_analyzer --file1 version1.txt --file2 version2.txt --mode lcs

# Calculate edit distance between strings
./text_analyzer --str1 "kitten" --str2 "sitting" --mode edit

# Find shortest common supersequence
./text_analyzer --file1 dna_seq_1.txt --file2 dna_seq_2.txt --mode scs

python generator.py

Generates two files with controlled mutation rate for testing:

• base_file.txt: Original text (configurable size)
• mutated_file.txt: Modified version with ~5% differences

// Reduced Edit Distance from O(n×m) to O(min(n,m))
// Critical for large-scale text processing
vector<int> prev(m + 1, 0);
vector<int> curr(m + 1, 0);

auto start = high_resolution_clock::now();
// Algorithm execution
auto stop = high_resolution_clock::now();
auto duration = duration_cast<milliseconds>(stop - start);

• File and string input support
• Comprehensive error checking
• Ambiguity detection
• Graceful failure modes

• Const-correctness throughout
• Reference parameters to avoid copying
• RAII for resource management
• Single Responsibility Principle

✅ Tabulation (Bottom-Up)
✅ Space Optimization Techniques
✅ Backtracking for Solution Construction
✅ State Transition Design
✅ Optimal Substructure Exploitation

✅ OOP Design Patterns
✅ Memory Management
✅ Performance Profiling
✅ CLI Design
✅ Error Handling
✅ Code Reusability

• Hirschberg's Algorithm: O(n) space LCS using divide-and-conquer
• Myers' Diff Algorithm: O(ND) time complexity for practical cases
• Parallel Processing: Multi-threaded DP for massive datasets
• GPU Acceleration: CUDA implementation for matrix operations

• Diff Visualization: HTML/colored terminal output
• Multiple Sequence Alignment: Extend to 3+ sequences
• Similarity Scoring: Percentage-based similarity metrics
• Batch Processing: Directory-level comparison
• JSON/XML Output: Structured result format
• Streaming Mode: Process files larger than RAM

• SIMD Instructions: Vectorized operations for character comparison
• Cache Optimization: Block-based DP computation
• Lazy Evaluation: Compute only required DP cells
• Compressed Storage: Sparse matrix representation

• Unit Testing Suite: Google Test integration
• Benchmarking Framework: Automated performance regression testing
• Configuration Files: YAML/JSON-based settings
• Logging System: Structured logging with levels
• API Wrapper: REST API for web integration

• Not just basic DP—demonstrates space optimization reducing memory by 99.98%
• Shows understanding of time-space tradeoffs
• Implements three different DP patterns in one project

• Comprehensive error handling and input validation
• Performance monitoring built-in
• Clean, maintainable architecture
• Const-correctness and modern C++ practices

• Solves actual industry problems (diff tools, spell checkers, bioinformatics)
• Handles large-scale data (20K+ character files)
• Practical CLI interface design

• Demonstrates profiling and optimization skills
• Space complexity reduced from O(n²) to O(n)
• Benchmarked and documented performance metrics

• C++ for performance-critical algorithms
• Python for tooling and test generation
• Understanding of entire development pipeline

• Clear documentation with complexity analysis
• Comprehensive README with examples
• Code comments where necessary

"I implemented three classic DP algorithms, but the key achievement was optimizing Edit Distance from O(n×m) to O(min(n,m)) space complexity using rolling arrays—reducing memory usage by 99.98% for large inputs."

"When dealing with 20,000+ character files, the naive O(n²) space approach would require 400MB. By recognizing we only need the previous row for computation, I reduced it to 80KB while maintaining O(n×m) time complexity."

"This tool can power diff algorithms in version control systems, spell checkers, or DNA sequence alignment—all requiring efficient string comparison at scale."

Where: n = length of string 1, m = length of string 2

✨ Space-Optimized Edit Distance: 99.98% memory reduction
✨ Multi-Algorithm Suite: 3 DP algorithms in one tool
✨ Production-Ready: Robust error handling & validation
✨ Performance Monitored: Built-in execution timing
✨ Flexible Interface: File and string input support
✨ Scalable: Handles 50K+ character inputs

Project: Part of DSA Project Roadmap
Phase: 3 - Advanced Intermediate (Project #14)
Status: ✅ Production Ready