{"id":25660066,"url":"https://github.com/truemagic-coder/pysafe-paper","last_synced_at":"2026-06-08T23:31:28.725Z","repository":{"id":278855030,"uuid":"936988822","full_name":"truemagic-coder/pysafe-paper","owner":"truemagic-coder","description":"PySafe: Enhancing Python with Memory Safety, Formal Verification, and Safe Concurrency","archived":false,"fork":false,"pushed_at":"2025-02-22T05:52:58.000Z","size":8,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-05-30T08:27:50.948Z","etag":null,"topics":["python"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/truemagic-coder.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-22T04:25:15.000Z","updated_at":"2025-02-22T05:53:02.000Z","dependencies_parsed_at":"2025-02-22T05:34:59.763Z","dependency_job_id":null,"html_url":"https://github.com/truemagic-coder/pysafe-paper","commit_stats":null,"previous_names":["truemagic-coder/pysafe-paper"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/truemagic-coder/pysafe-paper","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/truemagic-coder%2Fpysafe-paper","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/truemagic-coder%2Fpysafe-paper/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/truemagic-coder%2Fpysafe-paper/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/truemagic-coder%2Fpysafe-paper/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/truemagic-coder","download_url":"https://codeload.github.com/truemagic-coder/pysafe-paper/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/truemagic-coder%2Fpysafe-paper/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34085321,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["python"],"created_at":"2025-02-24T01:32:20.753Z","updated_at":"2026-06-08T23:31:28.706Z","avatar_url":"https://github.com/truemagic-coder.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# PySafe: Enhancing Python with Memory Safety, Formal Verification, and Safe Concurrency\n\nAuthor: Bevan Hunt, BBA\n\nDate: February 21, 2025\n\n## Abstract\n\nPython’s simplicity and widespread adoption make it a cornerstone of software development, yet its dynamic nature and limited concurrency support introduce runtime errors, performance bottlenecks, and concurrency-related bugs that hinder its use in critical and high-performance applications. We introduce PySafe, a superset of Python that augments the language with advanced safety, verification, and concurrency features while preserving its usability. PySafe integrates a borrow checker for memory safety, contracts for behavior specification, strict typing for error prevention, enhanced tuples for structured data, a prover for formal verification, safe concurrency mechanisms to prevent data races and deadlocks, and compilation to LLVM IR via SSA for performance. This paper details PySafe’s syntax and semantics, outlines its implementation, and demonstrates its benefits through examples, positioning PySafe as a robust extension of Python for modern, reliable systems.\n\n## 1. Introduction\n\nPython’s success stems from its intuitive syntax, rich ecosystem, and dynamic flexibility, making it a preferred choice for rapid development. However, these strengths come with trade-offs: runtime type errors, memory misuse, suboptimal performance in compute-intensive tasks, and limited support for safe parallelism due to the Global Interpreter Lock (GIL). These limitations restrict Python’s applicability in safety-critical systems, high-performance computing, and concurrent applications.\n\nTo address these challenges, we propose PySafe, a Python superset that introduces:\n- **Borrow Checker:** Ensures memory safety by tracking resource ownership and borrowing.\n- **Contracts:** Specify preconditions, postconditions, and invariants for verification.\n- **Strict Typing:** Enforces type safety with mandatory annotations and inference.\n- **Enhanced Tuples:** Add named fields and pattern matching for structured data.\n- **Prover:** Enables formal verification of program properties.\n- **Safe Concurrency:** Prevents data races and deadlocks in multi-threaded execution.\n- **LLVM IR Compilation via SSA:** Optimizes performance through native code generation.\n\nPySafe maintains compatibility with existing Python code, allowing developers to adopt its features incrementally.\n\nThis paper contributes:\n- A Python-compatible language with comprehensive safety, concurrency, and performance enhancements.\n- A detailed design of its syntax and semantics.\n- An implementation strategy for its compiler.\n- Use cases illustrating its advantages.\n\nThe paper is structured as follows: Section 2 reviews related work, Section 3 describes the language design, Section 4 explains the implementation, Section 5 evaluates PySafe through examples, and Section 6 discusses future directions.\n\n## 2. Related Work\n\nPySafe builds on concepts from several languages and tools:\n- **Rust:** Pioneered borrow checking for memory safety and safe concurrency without garbage collection [1].\n- **Eiffel:** Introduced design by contract with formal specifications [2].\n- **mypy:** Offers static type checking for Python via optional annotations [3].\n- **Dafny:** Integrates contracts and formal verification with a prover [4].\n- **Numba:** Uses LLVM to JIT-compile Python for performance [5].\n- **Cython:** Adds static typing to Python for speed [6].\n- **PyPy:** A JIT-compiled Python implementation [7].\n- **Mojo:** A Python superset by Modular with borrow checking and optimizations [8].\n\nRust’s ownership model ensures thread safety, while Go’s goroutines and channels (though not cited here) inspire concurrency alternatives. Python’s GIL limits multi-threading, relying on multiprocessing for parallelism, which lacks fine-grained safety. PySafe uniquely combines these features into a Python-compatible framework.\n\n[1] Matsakis, N. D., \u0026 Klock, F. S. (2014). The Rust language. ACM SIGAda Ada Letters, 34(2), 103-104.  \n[2] Meyer, B. (1992). Applying \"design by contract\". Computer, 25(10), 40-51.  \n[3] Lehtosalo, J. (2016). Static types for Python. ACM SIGPLAN Python Symposium.  \n[4] Leino, K. R. M. (2010). Dafny: An automatic program verifier. LPAR, 6138, 348-370.  \n[5] Lam, S. K., et al. (2015). Numba: A LLVM-based Python JIT compiler. LLVM-HPC, 1-6.  \n[6] Behnel, S., et al. (2011). Cython: The best of both worlds. Computing in Science \u0026 Engineering, 13(2), 31-39.  \n[7] Rigo, A., \u0026 Pedroni, S. (2006). PyPy's approach to virtual machine construction. OOPSLA, 944-953.  \n[8] Modular Mojo Programming Language. (n.d.). Retrieved from https://modular.com/\n\n## 3. Language Design\n\nPySafe extends Python with syntax and semantics for safety, verification, and concurrency.\n\n### 3.1 Borrow Checker\n\nPySafe's borrow checker, inspired by Rust, ensures memory safety using `own` and `borrow` annotations:\n\n```python\ndef transfer(a: borrow int, b: own int) -\u003e int:\n    return a + b  # 'a' is immutable; 'b' is owned\n```\n\nIt prevents invalid memory access and extends to concurrency by enforcing thread-safe data access.\n\n### 3.2 Contracts\n\nContracts specify behavior with decorators:\n\n```python\n@pre(lambda self: self.balance \u003e= 0)\n@post(lambda self, result: self.balance == old(self).balance - result)\ndef withdraw(self, amount: int) -\u003e int:\n    self.balance -= amount\n    return amount\n```\n\n`@pre` and `@post` define conditions, with `old` referencing pre-execution state, applicable to both single-threaded and concurrent contexts.\n\n### 3.3 Strict Typing\n\nPySafe mandates type annotations with inference:\n\n```python\ndef add(a: int, b: int) -\u003e int:\n    return a + b\n```\n\nThis ensures type safety across threads and compile-time error detection.\n\n### 3.4 Enhanced Tuples\n\nTuples gain named fields and pattern matching:\n\n```python\npoint = (x=10, y=20)\nprint(point.x)  # Outputs 10\n```\n\nThis supports structured data in concurrent programs.\n\n### 3.5 Prover Integration\n\nThe `@prove` decorator enables formal verification:\n\n```python\n@prove(\"forall x in range(10): x \u003e= 0\")\ndef positive_numbers() -\u003e List[int]:\n    return [i for i in range(10)]\n```\n\nIt verifies properties, including concurrency invariants.\n\n### 3.6 Safe Concurrency\n\nPySafe introduces safe concurrency using the borrow checker and new primitives like `shared`:\n\n```python\nshared_data: shared List[int] = [1, 2, 3]\n\ndef worker():\n    with lock(shared_data):\n        shared_data.append(4)  # Thread-safe access\n```\n\nThe borrow checker ensures exclusive mutable access or multiple immutable reads, preventing data races. This builds on Python's threading model, bypassing the GIL where possible.\n\n### 3.7 Compilation to LLVM IR\n\nPySafe compiles to LLVM IR via SSA, optimizing performance for concurrent and sequential code.\n\n## 4. Implementation\n\nThe PySafe compiler pipeline includes:\n- **Parsing:** Ratpack builds an AST from PySafe code.\n- **Type Checking:** Validates types using a Hindley-Milner-inspired system.\n- **Borrow Checking:** Tracks ownership and borrowing across threads.\n- **Contract Checking:** Validates contracts dynamically or statically.\n- **Concurrency Analysis:** Ensures thread safety, integrating with the borrow checker.\n- **SSA Transformation:** Converts the AST to SSA for optimization.\n- **LLVM IR Generation:** Produces optimized machine code via LLVM.\n\nThe prover operates optionally for `@prove` annotations. The compiler will be first implemented in Rust and then bootstrapped using PySafe to compile subsequent versions of the compiler.\n\n## 5. Evaluation\n\nAs a conceptual design, PySafe's benefits are illustrated through examples:\n\n### 5.1 Memory Safety\n\nThe borrow checker prevents unsafe operations:\n\n```python\nlst: own List[int] = [1, 2, 3]\nfor x: borrow int in lst:\n    # lst.append(4)  # Blocked by borrow checker\n    print(x)\n```\n\n### 5.2 Contract Verification\n\nA banking system ensures correctness:\n\n```python\nclass Account:\n    balance: int = 0\n\n    @invariant(lambda self: self.balance \u003e= 0)\n    @post(lambda self, result: self.balance == old(self).balance + result)\n    def deposit(self, amount: int) -\u003e int:\n        self.balance += amount\n        return amount\n```\n\nThe prover confirms invariants hold.\n\n### 5.3 Safe Concurrency\n\nA concurrent counter avoids data races:\n\n```python\ncounter: shared int = 0\n\ndef increment():\n    with lock(counter):\n        counter += 1  # Safe, exclusive access\n```\n\nUnlike Python, where race conditions could occur without locks, PySafe ensures safety at compile time.\n\n### 5.4 Performance\n\nCompiled matrix multiplication with concurrent threads could outperform Python's interpreted execution, leveraging LLVM optimizations.\n\nThese examples highlight PySafe's potential, awaiting implementation.\n\n## 6. Conclusion\n\nPySafe enhances Python with memory safety, formal verification, safe concurrency, and performance features, maintaining compatibility and usability. By integrating a borrow checker, contracts, strict typing, enhanced tuples, a prover, concurrency primitives, and LLVM compilation, PySafe offers a foundation for reliable, efficient, and concurrent Python programming. Future work includes refining syntax, implementing full concurrency support beyond initial single-threaded features, building the compiler, and conducting empirical evaluations to validate its user benefits.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftruemagic-coder%2Fpysafe-paper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftruemagic-coder%2Fpysafe-paper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftruemagic-coder%2Fpysafe-paper/lists"}