Retargeting an Abstract Interpreter for a New Language by Partial Evaluation

2025-07-06

AI Summary

Reimplementing abstract interpreters for new programming languages incurs high development costs. Method: This paper proposes an automatic abstract interpreter retargeting technique based on partial evaluation. It embeds the formal semantics of a target language into the source language of an existing abstract interpreter and achieves cross-language analyzer migration via semantics-driven specialization—requiring neither manual rewriting nor adaptation. Contribution/Results: The core innovation lies in the tight integration of semantic modeling, abstract interpretation, and partial evaluation to guarantee both semantic correctness and analysis precision of the migrated analyzer. Experimental evaluation demonstrates the method’s feasibility and effectiveness across diverse programming languages, significantly improving static analyzer reuse efficiency and construction speed while preserving soundness and precision.

Technology Category

Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsKnowledge Representation and Reasoning: Automated Reasoning and Theorem ProvingConstraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

It is well-known that abstract interpreters can be systematically derived from their concrete counterparts using a "recipe," but developing sound static analyzers remains a time-consuming task. Reducing the effort required and mechanizing the process of developing analyzers continues to be a significant challenge. Is it possible to automatically retarget an existing abstract interpreter for a new language? We propose a novel technique to automatically derive abstract interpreters for various languages from an existing abstract interpreter. By leveraging partial evaluation, we specialize an abstract interpreter for a source language. The specialization is performed using the semantics of target languages written in the source language. Our approach eliminates the need to develop analyzers for new targets from scratch. We show that our method can effectively retarget an abstract interpreter for one language into a correct analyzer for another language.

Problem

• Automatically retarget abstract interpreters for new languages
• Reduce effort in developing sound static analyzers
• Specialize interpreters using partial evaluation and semantics

Innovation

• Specialize abstract interpreter via partial evaluation
• Use source language semantics for target languages
• Automatically derive analyzers for new languages