Compiling with Abstract Interpretation
Dorian Lesbre, Matthieu Lemerre
摘要
Rewriting and static analyses are mutually beneficial techniques: program transformations change the inten- sional aspects of the program, and can thus improve analysis precision, while some efficient transformations are enabled by specific knowledge of some program invariants. Despite the strong interaction between these techniques, they are usually considered distinct. In this paper, we demonstrate that we can turn abstract interpreters into compilers, using a simple free algebra over the standard signature of abstract domains. Functor domains correspond to compiler passes, for which soundness is translated to a proof of forward simulation, and completeness to backward simulation. We achieve translation to SSA using an abstract domain with a non-standard SSA signature. Incorporating such an SSA translation to an abstract interpreter improves its precision; in particular we show that an SSA-based non-relational domain is always more precise than a standard non-relational domain for similar time and memory complexity. Moreover, such a domain allows recovering from precision losses that occur when analyzing low-level machine code instead of source code. These results help implement analyses or compilation passes where symbolic and semantic methods simultaneously refine each other, and improves precision when compared to doing the passes in sequence. CCS Concepts: • Software and its engineering → Compilers ; Formal Software verification; • Theory of computation → Program analysis ; Program verification ; Abstraction ; Equational logic and rewriting.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Relational Abstractions Based on Labeled Union-FindDorian Lesbre, Matthieu Lemerre, Hichem Rami Ait El Hara, François BobotPLDI 2025 · 被引用 3 次
- Optimism in Equality SaturationRussel Arbore, Alvin Cheung, Max WillseyPLDI 2026
- A Categorical Basis for Robust Program AnalysisZachary Kincaid, Shaowei ZhuPLDI 2026
它引用的顶会 Paper2
相关 Paper
- Nice to Meet You: Synthesizing Practical MLIR Abstract TransformersXuanyu Peng, Dominic Kennedy, Yuyou Fan, Ben Greenman 等POPL 2026
- Program Analysis Combining Generalized Bit-Level and Word-Level AbstractionsGuangsheng Fan, Liqian Chen, Banghu Yin, Wenyu Zhang 等ISSTA 2025 · 被引用 2 次
- Synthesizing abstract transformersPankaj Kumar Kalita, Sujit Kumar Muduli, Loris D'Antoni, Thomas W. Reps 等OOPSLA 2022 · 被引用 21 次
- Abstract Interpretation with Confidence: Quantifying the Precision of Dataflow Analysis with ProbabilitiesYuanfeng Shi, Ziyue Jin, Xin ZhangPLDI 2026
- Program analysis via efficient symbolic abstractionPeisen Yao, Qingkai Shi, Heqing Huang, Charles ZhangOOPSLA 2021 · 被引用 12 次
