A Modular Framework for Stack-Heap and Value Abstractions (Extended Version)
Summary
A new modular framework is proposed for combined static analysis of stack and heap memory, designed to support diverse programming language behaviors like those in C, C++, Java, and Python. This generic framework, formalized over μ͢LL, relies on Abstract Interpretation theory to soundly approximate various memory models and program behaviors. Its core innovation is a "split state" abstraction, which separates value and memory analyses into two distinct, modular abstract domains. These domains interact through "memory identifiers" and specific operations, enabling the framework to capture both value information and structural memory relationships. The approach extends prior work by supporting arbitrary references, dereferences, and dynamic field accesses, moving beyond object-oriented language constraints, and ensures no precision loss through a Galois Isomorphism between concrete and split states.
Key takeaway
For research scientists and software engineers aiming to improve static program analysis precision, this modular framework offers a robust method to integrate memory and value analyses. By leveraging the "split state" abstraction and memory identifiers, you can detect critical issues like buffer overflows and use-after-free errors across languages like C, C++, Java, and Python. Consider adopting this approach to enhance your static analysis tools and ensure more accurate pre-execution bug detection.
Key insights
A modular framework separates memory and value analyses using a split state and memory identifiers for precise static program analysis.
Principles
- Static analysis benefits from modular memory/value separation.
- Galois Isomorphism ensures precision in state abstraction.
- Memory identifiers facilitate domain interaction.
Method
The framework formalizes a "split state" abstraction over μ͢LL, separating value and memory domains. These domains cooperate via a lightweight interface and substitution mechanism, using memory identifiers for interaction.
In practice
- Detect buffer overflows and use-after-free errors.
- Combine different memory and value analysis techniques.
- Analyze C, C++, Java, and Python memory models.
Topics
- Static Program Analysis
- Memory Abstraction
- Abstract Interpretation
- Stack-Heap Analysis
- Value Analysis
- Pointer Analysis
Code references
Best for: AI Scientist, Research Scientist, Software Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.