Daily Backlog Burner - Research, Roadmap and Plan

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Daily Backlog Burner - Research, Roadmap and Plan

Overview

Z3 is a mature theorem prover from Microsoft Research with a substantial codebase and active community. After comprehensive analysis of the repository structure, open issues, pull requests, and project documentation, this document outlines a strategic roadmap for addressing the project's backlog through systematic improvements.

Project Analysis

Core Project Information

• Primary Language: C++ (C++20 standard)
• Build Systems: CMake (recommended), Python mk_make.py, Bazel, vcpkg
• Key Components: SAT solver, SMT theories, API bindings (C, C++, Python, Java, .NET, OCaml)
• Testing: Custom unit testing framework, regression tests
• Platforms: Windows, Linux, macOS, with specialized builds for Android, WASM, Pyodide

Current Status Assessment

**(redacted)

• Well-maintained API documentation and release process
• Active development with regular releases (latest: 4.15.3)
• Strong performance focus with ongoing optimization work
• Comprehensive language bindings and platform support
• Good CI/CD pipeline with Azure Pipelines and GitHub Actions

**Areas Requiring (redacted)

• Development tooling could be modernized (lacks .clang-format file)
• Unit testing framework is homegrown and could benefit from standardization
• Some long-standing enhancement requests remain unaddressed
• Build/release process has some platform-specific issues

Backlog Analysis

High-Priority Bugs (7 open)

• Performance regressions: Version 4.13.3 → 4.15.2 convergence issues
• API inconsistencies: Python API vs CLI result discrepancies
• Model validation: Invalid model generation in string/sequence operations
• Arithmetic optimization: Correctness issues in opt.optsmt_engine=symba

Development Infrastructure Enhancements (Help Wanted: 3 open)

1. Code formatting standardization (Clang-format file #1441) - Add .clang-format file
2. Build system improvements (Bad .dylib versioning in pip package. #6651) - Fix .dylib versioning in pip packages
3. Java packaging (Bundling Dynamic Libraries Inside JAR for Ease of Redistribution #182) - Bundle dynamic libraries in JAR files

Long-term Feature Requests (8 open)

• Python API enhancements: Regex translator for Python API (Feature request: add regex translator to Python API #5860)
• Persistence features: Solver state save/restore functionality (Feature: Persisting the solver state and Resuming from it. #2095)
• Build improvements: Include folder organization (Create a include folder #1664)
• Testing modernization: Replace homegrown unit test framework (Unit tests need clean up #1163)

Strategic Roadmap

Phase 1: Development Infrastructure (Immediate Impact)

Priority: High | Timeline: 2-4 weeks | Impact: Developer Experience

1. Add .clang-format file (Clang-format file #1441)

   • Analyze existing C++ code style patterns
   • Create comprehensive .clang-format configuration
   • Test with existing codebase to ensure compatibility
   • Document usage in developer guide

2. Fix build/packaging issues

   • Address .dylib versioning in pip packages (Bad .dylib versioning in pip package. #6651)
   • Investigate and resolve platform-specific build inconsistencies

3. Testing infrastructure improvements

   • Evaluate modern C++ testing frameworks (GoogleTest, Catch2)
   • Create migration plan for existing unit tests
   • Address Windows-specific test guards that limit CI coverage

Phase 2: Code Quality & Maintenance (Foundation Building)

Priority: Medium-High | Timeline: 4-6 weeks | Impact: Long-term Maintainability

1. Unit test modernization (Unit tests need clean up #1163)

   • Replace SASSERT-based tests with proper assertions
   • Remove platform-specific guards where unnecessary
   • Implement proper error handling and exit codes

2. Documentation improvements

   • Create/update developer contribution guidelines
   • Enhance API documentation generation process
   • Improve build documentation for all supported platforms

3. Code organization

   • Create proper include folder structure (Create a include folder #1664)
   • Review and organize header file distribution

Phase 3: Bug Resolution & Performance (User Impact)

Priority: High | Timeline: 3-5 weeks | Impact: User Experience

1. Critical bug fixes

   • Performance regression analysis (Convergence regression when migrating from 4.13.3 to 4.15.2 #7697)
   • API consistency issues (Inconsistent results in CLI and Z3Py API #7687, invalid model #7664)
   • Arithmetic optimization correctness (Potential issue in arithmetic optimization #7677)

2. Long-standing correctness issues

   • z3str3 BV formula incorrect answers (qe z3str3 incorrect answer on BV formula #3047)
   • Model validation improvements

Phase 4: Feature Enhancements (Community Value)

Priority: Medium | Timeline: 6-8 weeks | Impact: API Usability

1. Python API improvements (Feature request: add regex translator to Python API #5860)

   • Implement regex translator functionality
   • Enhance API documentation and examples

2. Java ecosystem improvements (Bundling Dynamic Libraries Inside JAR for Ease of Redistribution #182)

   • Bundle dynamic libraries in JAR distributions
   • Simplify Java integration workflow

Phase 5: Advanced Features (Strategic)

Priority: Lower | Timeline: 8-12 weeks | Impact: Advanced Use Cases

1. Solver persistence (Feature: Persisting the solver state and Resuming from it. #2095)

   • Research state serialization feasibility
   • Prototype save/restore functionality for incremental solving

2. Performance optimizations

   • Continue work that doesn't overlap with existing performance PRs
   • Focus on complementary areas not covered by current efforts

Risk Mitigation & Constraints

Active Development Conflicts

• Performance PRs: Multiple performance improvement PRs are currently active (Daily Perf Improver: Optimize small object allocator with pool-based allocation #7885, Daily Perf Improver: Optimize hashtable cache locality and load factor #7887, Daily Perf Improver: Cache-friendly clause management optimization #7889, Daily Perf Improver: SIMD Vectorization for BitVector Operations (Round 2) #7892, Daily Perf Improver: VSIDS Heap and Activity Management Optimizations (Round 2) #7894)
• Mitigation: Avoid overlapping with these areas; focus on orthogonal improvements

Compatibility Considerations

• API Stability: Maintain backward compatibility for all public APIs
• Platform Support: Ensure changes work across all supported platforms
• Build System: Support all existing build methods (CMake, Python, Bazel)

Testing Strategy

• Regression Prevention: Comprehensive testing before merging changes
• CI Integration: Ensure all changes pass existing CI/CD pipelines
• Performance Validation: Benchmark performance-impacting changes

Success Metrics

Phase 1 Success Criteria

• .clang-format file added and verified with existing codebase
• pip package .dylib versioning fixed
• At least 2 help-wanted issues resolved

Phase 2 Success Criteria

• Modern unit testing framework integrated
• Developer documentation improved
• Include folder structure implemented

Phase 3 Success Criteria

• Critical performance regressions resolved
• API consistency issues fixed
• All high-priority bugs addressed

Implementation Approach

Development Methodology

1. Conservative Changes: Prioritize backward compatibility
2. Incremental Progress: Small, reviewable pull requests
3. Community Engagement: Regular updates and maintainer feedback
4. Quality Assurance: Comprehensive testing for each change

Collaboration Strategy

• Maintainer Engagement: Regular communication with core team
• Community Feedback: Incorporate user and contributor input
• Documentation: Maintain detailed progress tracking
• Knowledge Sharing: Document decisions and rationale

Conclusion

This roadmap provides a systematic approach to improving Z3's development experience, code quality, and user satisfaction. By focusing on infrastructure first, then quality improvements, and finally feature enhancements, we can build a solid foundation for continued project success.

The phased approach ensures that each improvement builds upon previous work while minimizing risk to the stable, production-ready theorem prover that Z3 represents. Regular reassessment and community feedback will guide prioritization adjustments as work progresses.

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