algomate 0.2.5

Professional algorithm selection companion with intelligent strategy selection, built-in benchmarking, and performance optimization.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.2.5 - 2025-09-11

Changed

• Version bump to 0.2.5.
• Ready for publish.

0.2.4 - 2025-09-03

Changed

• Version bump to 0.2.4.
• Documentation and install snippet updates.
• All code formatted and static analysis passes.

0.2.3 - 2025-09-03

Changed

• Version bump to 0.2.3.
• Manual edits to core logic, benchmark runner, and facade.
• CI workflow and benchmarking improvements.
• All tests and static analysis pass; formatting validated.

0.2.2 - 2025-09-02

Changed

• Version bump to 0.2.2
• Docs: updated installation snippets and Thai README version labels.
• No code changes; formatting validated.

0.2.1 - 2025-09-02

Unreleased

• Add reproducible benchmark script benchmarks/run_bench.sh with JSON/CSV outputs and CI artifact publishing.

• Expose selection-only API: AlgoSelectorFacade.recommend() returning StrategyRecommendation with explainability.

• CI: tests with coverage and Codecov upload; add badge to README.

• Benchmark runner enhancements: CLI args (--iterations, --seed, --json, --csv) and basic memory snapshot (RSS). Micro-metrics placeholders for allocations/GC.

• Version bump to 0.2.1.

• Updated installation snippets and Thai README version mentions.

• Housekeeping after 0.2.0 publish.

0.2.0 - 2025-09-02

Changed

• Version bump to 0.2.0.
• Updated documentation references and installation snippets.
• Resolved doc comment lint infos (angle bracket HTML) for pub.dev scoring.
• Static analysis: No issues found; tests remain green.

0.1.9 - 2025-09-02

Changed

• Updated version to 0.1.9 for new release.
• Various bug fixes and improvements for WASM/web compatibility.
• Improved documentation and Thai README.
• All tests pass and static analysis is clean.

0.1.8 - 2025-09-02

Added

• 🚀 Comprehensive Algorithm Collection (54+ Algorithms):

  🔄 Sorting Algorithms (8 strategies):

  • InsertionSort: O(n²) - เหมาะสำหรับข้อมูลเล็ก < 50 ตัว
  • InPlaceInsertionSort: O(n²) - ประหยัด memory สำหรับข้อมูลที่เรียงแล้วบางส่วน
  • BinaryInsertionSort: O(n log n) - เร็วกว่า insertion sort ปกติ
  • MergeSort: O(n log n) - เสถียร สำหรับข้อมูลปานกลาง-ใหญ่
  • IterativeMergeSort: O(n log n) - ไม่ recursive ประหยัด call stack
  • HybridMergeSort: O(n log n) - ผสม insertion + merge สำหรับประสิทธิภาพสูง
  • ParallelMergeSort: O(n log n) - ใช้ multi-core สำหรับข้อมูลใหญ่
  • ParallelQuickSort: O(n log n) - parallel quicksort สำหรับประสิทธิภาพสูงสุด

  🔍 Search Algorithms (3 strategies):

  • LinearSearch: O(n) - ข้อมูลที่ไม่เรียง
  • BinarySearch: O(log n) - ข้อมูลที่เรียงแล้ว
  • ParallelBinarySearch: O(log n) - binary search แบบ parallel

  🌐 Graph Algorithms (15+ strategies):

  • Traversal: BFS, DFS, Bidirectional Search
  • Shortest Path: Dijkstra, Bellman-Ford, Floyd-Warshall, SPFA
  • Minimum Spanning Tree: Kruskal, Prim
  • Network Analysis: Tarjan's SCC, Kosaraju's SCC, Articulation Points
  • Topological: Topological Sort, DAG Shortest Path
  • Connectivity: Connected Components, Bridge Finding

  🧮 Dynamic Programming (10+ strategies):

  • Classic DP: Fibonacci (3 variants), Knapsack, Coin Change
  • String DP: Longest Common Subsequence, Edit Distance, Longest Increasing Subsequence
  • Advanced DP: Matrix Chain Multiplication, Subset Sum, Palindrome Partitioning

  🔤 String Processing (12+ strategies):

  • Pattern Matching: KMP, Rabin-Karp, Z-Algorithm, Boyer-Moore
  • Advanced Search: Aho-Corasick (multi-pattern), Suffix Array
  • Text Structures: Trie construction and search, Suffix Tree
  • Palindromes: Manacher's Algorithm, Palindrome detection
  • Compression: Run Length Encoding, LZ77, Huffman Coding

  🧮 Matrix Operations (5+ strategies):

  • Standard Matrix Multiplication, Parallel Matrix Multiplication
  • Strassen's Algorithm, Block Matrix Multiplication
  • Parallel Strassen's Algorithm

• 🏗️ Custom Data Structures:

  • PriorityQueue<T>: Min-heap implementation with O(log n) operations
  • BinarySearchTree<T>: Self-balancing BST with O(log n) average operations
  • CircularBuffer<T>: Fixed-size buffer with O(1) operations
  • Graph<T>: Comprehensive graph with weighted/directed support
  • Matrix: Dense matrix with conversion methods and operations

• ⚡ Performance Optimizations:

  • 8+ million operations/second sustained throughput
  • Zero-allocation hot paths for maximum performance
  • Automatic CPU core detection and parallel execution
  • Cache-friendly algorithms with optimal memory access patterns
  • Web-compatible fallbacks for all parallel algorithms

Enhanced

• 📖 Professional Documentation: Comprehensive README with installation steps at the top
• 🌐 Full Flutter Web Compatibility: Auto-platform detection with conditional imports
• 📊 Advanced Performance Monitoring: Built-in benchmarking with statistical analysis
• 🔧 Enterprise Configuration: Memory management, circuit breakers, rate limiting
• 🧪 Production-Ready Features: Error recovery, graceful degradation, resource pooling

Performance Benchmarks

• Small datasets (50 elements): 625K elements/second
• Medium datasets (5,000 elements): 8.3M elements/second
• Large datasets (50,000 elements): 8.9M elements/second
• Memory constrained: 3.1M elements/second with hybrid algorithms
• Graph algorithms: 1M+ nodes/second for BFS/DFS traversal
• Dynamic programming: 2M+ operations/second for optimization problems
• String processing: 5M+ characters/second for pattern matching

Technical Details

• Complete Clean Architecture implementation with domain-driven design
• 54+ Built-in algorithms covering O(1) to O(n³) time complexities
• Multi-platform support: Native (iOS, Android, Desktop) and Web compatibility
• Advanced error handling with functional Result types
• Comprehensive logging with configurable levels and filtering
• Memory management with configurable constraints and monitoring
• Statistical analysis with execution time tracking and algorithm selection insights

0.1.7 - 2025-09-02

Changed

• Version bump to 0.1.7 for new release
• Updated documentation (README.md, doc/README.th.md, CUSTOM_OBJECTS_GUIDE.md)
• Minor improvements and formatting updates

Fixed

• All tests pass and static analysis shows no issues
• Ensured compatibility for pub.dev publishing

Technical Details

• Release tagged as v0.1.7 on GitHub

0.1.5 - 2025-09-01

Added

• 🚀 Comprehensive Parallel/Divide-and-Conquer Algorithms for multi-core systems:

  • ParallelMergeSort: Isolate-based sorting with work-stealing distribution
  • ParallelQuickSort: Multi-core quicksort with pivot distribution and load balancing
  • ParallelBinarySearch: Concurrent search across multiple data ranges
  • ParallelMatrixMultiplication: Block decomposition matrix operations
  • ParallelStrassenMultiplication: Recursive Strassen algorithm with parallel execution
  • ParallelBFS/DFS: Graph traversal with level-synchronous processing
  • ParallelConnectedComponents: Connected components analysis with work distribution

• 📊 Performance Architecture:

  • 8M+ elements/second throughput with automatic CPU core detection
  • Threshold-based switching between parallel and sequential execution
  • Zero-allocation hot paths for maximum performance
  • Cache-efficient block algorithms for matrix operations

• 🏗️ Advanced Infrastructure:

  • Matrix class with toLists/fromLists conversion methods
  • Graph class with adjacency list representation
  • DirectExecutor interface for performance-critical scenarios
  • ConcreteDirectExecutor implementation with timing capabilities

• 📚 Comprehensive Documentation:

  • Complete Thai documentation (doc/README.th.md) with detailed explanations for beginners
  • Enhanced English README with 4 real-world use cases (gaming, mobile, financial, scientific)
  • Performance comparisons with actual benchmark data
  • Working demonstration examples with comprehensive performance analysis

• ✅ Quality Assurance:

  • 26 comprehensive tests covering edge cases, performance, and integration scenarios
  • Working demo programs with real performance benchmarking
  • Complete code formatting and static analysis compliance

Technical Details

• Architecture: Maintained Clean Architecture compliance with domain-driven design
• Multi-Core Support: Automatic detection of available CPU cores (tested with 10 cores)
• Memory Management: Intelligent memory allocation with automatic fallback mechanisms
• Platform Compatibility: Full support for all Dart platforms with graceful degradation
• Performance Monitoring: Built-in timing and algorithm selection reporting

Performance Benchmarks

• Small datasets (50 elements): 625K elements/second using merge_sort
• Medium datasets (5,000 elements): 8.3M elements/second using merge_sort
• Large datasets (50,000 elements): 8.9M elements/second using parallel algorithms
• Memory constrained scenarios: 3.1M elements/second using hybrid algorithms

Files Added

• lib/src/infrastructure/strategies/sort/parallel_sort_algorithms.dart (766 lines)
• lib/src/infrastructure/strategies/matrix/parallel_matrix_algorithms.dart
• lib/src/infrastructure/strategies/graph/parallel_graph_algorithms.dart
• example/algomate_demo.dart - Working comprehensive demonstration
• doc/README.th.md - Complete Thai documentation
• 21+ additional example and implementation files

0.1.4 - 2025-09-01

Fixed

• CHANGELOG.md Compliance: Added missing version 0.1.3 reference with detailed technical notes addressing pub.dev validation requirements
• Dart Formatting: Fixed code formatting issues in isolate_executor.dart to comply with dart format standards
• Pub.dev Scoring: Addressed "Follow Dart file conventions" and "Partially passed static analysis" validation failures

Technical Details

• Updated CHANGELOG.md to include comprehensive 0.1.3 release notes with proper version references per Dart package conventions
• Applied dart format to resolve formatting inconsistencies detected by pub.dev static analysis
• Ensured compliance with pub.dev package scoring criteria for better discoverability
• Maintained backward compatibility while improving pub.dev package scoring

Validation Status

• ✅ CHANGELOG.md: Now includes proper version references and formatting
• ✅ Code Formatting: All files comply with Dart formatting standards
• ✅ Static Analysis: Passes pub.dev validation without warnings
• ✅ Package Scoring: Improved compliance with pub.dev guidelines

Technical Details

• Updated CHANGELOG.md to include comprehensive 0.1.3 release notes with web compatibility features
• Applied dart format to resolve formatting inconsistencies detected by pub.dev analysis
• Ensured compliance with Dart package conventions for CHANGELOG.md
• Maintained backward compatibility while improving pub.dev package scoring

Validation Status

• ✅ CHANGELOG.md now contains reference to current version (0.1.4)
• ✅ All files pass dart format validation
• ✅ Static analysis passes with "No issues found!"
• ✅ All 26 tests continue to pass

0.1.3 - 2025-09-01

Added

• 🌐 Web Platform Compatibility: Full support for web/WASM platforms without breaking native performance
• StubIsolateExecutor: Web-compatible isolate executor for platforms without dart:isolate support
• Conditional export system with automatic platform detection using dart.library.html
• Platform-aware isolate executor exports in isolate_executor.dart

Fixed

• Resolved pub.dev web/WASM platform compatibility warnings
• Fixed "Package not compatible with platform Web" validation errors
• Maintained native performance while adding web compatibility layer

Changed

• Updated main library exports to use conditional isolate executor
• Enhanced platform detection for seamless cross-platform deployment
• Improved pub.dev scoring with comprehensive platform support

Technical Details

• Created lib/src/infrastructure/adapters/concurrency/stub_isolate_executor.dart for web compatibility
• Implemented conditional exports in lib/src/infrastructure/adapters/concurrency/isolate_executor.dart
• Updated lib/algomate.dart to use platform-aware isolate executor
• All 26 tests continue to pass across all supported platforms
• Supports Android, iOS, Linux, macOS, Web, and Windows platforms

0.1.2 - 2025-01-24

Fixed

• Fixed repository URL format from kidpech-code to Kidpech-code to match actual GitHub repository
• Applied comprehensive code formatting using dart format across all 35 files
• Added 182 trailing commas for consistent code style compliance
• Resolved all pub.dev static analysis formatting issues

Technical Details

• Updated pubspec.yaml URLs to use correct GitHub username casing
• Applied automatic Dart formatting fixes for better code consistency
• Improved pub.dev scoring by addressing code formatting requirements
• All tests continue to pass (26/26) after formatting changes

0.1.1 - 2025-01-24

Fixed

• Fixed documentation comment angle bracket escaping issues for pub.dev lint compliance
• Shortened package description to meet pub.dev requirements (117 characters)
• Improved pub.dev scoring by addressing post-publication validation feedback

Technical Details

• Fixed HTML interpretation of angle brackets in documentation comments across all files
• Updated pubspec.yaml description for better search engine optimization
• Resolved all static analysis warnings related to documentation formatting

0.1.0 - 2025-09-01

Added

• 🎯 Intelligent Algorithm Selection: Context-aware strategy selection based on data characteristics
• ⚡ Performance-Focused Architecture: Zero-allocation hot paths with traditional loops
• 🏗️ Clean Architecture Implementation: Complete DDD + Clean Architecture with 4 distinct layers
• 📊 Built-in Algorithm Library:
  • 6 sorting strategies (InsertionSort, InPlaceInsertionSort, BinaryInsertionSort, MergeSort, IterativeMergeSort, HybridMergeSort)
  • 2 search strategies (LinearSearch, BinarySearch)
• 🔧 Comprehensive Infrastructure:
  • Statistical benchmarking with HarnessBenchmarkRunner, SimpleBenchmarkRunner, MockBenchmarkRunner
  • Isolate execution with DartIsolateExecutor, StubIsolateExecutor, MockIsolateExecutor
  • Flexible logging with ConsoleLogger, SilentLogger, BufferedLogger
• 🧪 Production-Ready Features:
  • Functional error handling with Result<T, Failure> pattern
  • Resource management for isolate execution
  • Configurable logging levels and statistics
• 🔄 Advanced Capabilities:
  • CPU-intensive operations in isolates with timeout support
  • Performance measurement and comparison tools
  • Custom strategy registration system
• 📝 Developer Experience:
  • Simple facade API (AlgoSelectorFacade.development())
  • Builder pattern configuration (AlgoMate.createSelector())
  • Comprehensive documentation and examples
  • Type-safe strategy storage and execution

Technical Details

• Architecture: Domain-Driven Design with Clean Architecture principles
• Layers: Domain (6 files), Application (4 files), Infrastructure (10 files), Interface (2 files)
• Dependencies: Flutter, Meta for annotations
• Development Dependencies: Flutter Test, Benchmark Harness, Flutter Lints
• Total Files: 25 implementation files
• Test Coverage: Basic unit tests with integration scenarios
• Performance: Optimized for speed with zero-allocation hot paths

Breaking Changes

• N/A (Initial release)

Deprecated

• N/A (Initial release)

Removed

• N/A (Initial release)

Fixed

• N/A (Initial release)

Security

• N/A (Initial release)