Polynomial reachability witnesses via Stellensätze
Ali Asadi, Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady, Mohammad Mahdavi
摘要
We consider the fundamental problem of reachability analysis over imperative programs with real variables. Previous works that tackle reachability are either unable to handle programs consisting of general loops (e.g. symbolic execution), or lack completeness guarantees (e.g. abstract interpretation), or are not automated (e.g. incorrectness logic). In contrast, we propose a novel approach for reachability analysis that can handle general and complex loops, is complete, and can be entirely automated for a wide family of programs. Through the notion of Inductive Reachability Witnesses (IRWs), our approach extends ideas from both invariant generation and termination to reachability analysis.
We first show that our IRW-based approach is sound and complete for reachability analysis of imperative programs. Then, we focus on linear and polynomial programs and develop automated methods for synthesizing linear and polynomial IRWs. In the linear case, we follow the well-known approaches using Farkas' Lemma. Our main contribution is in the polynomial case, where we present a push-button semicomplete algorithm. We achieve this using a novel combination of classical theorems in real algebraic geometry, such as Putinar's Positivstellensatz and Hilbert's Strong Nullstellensatz. Finally, our experimental results show we can prove complex reachability objectives over various benchmarks that were beyond the reach of previous methods.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,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 Paper21
- Finding real bugs in big programs with incorrectness logicQuang Loc Le, Azalea Raad, Jules Villard, Josh Berdine 等OOPSLA 2022 · 被引用 52 次
- Sound and Complete Certificates for Quantitative Termination Analysis of Probabilistic ProgramsKrishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde ZikelicCAV 2022 · 被引用 30 次
- Quantitative Bounds on Resource Usage of Probabilistic ProgramsKrishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde ZikelicOOPSLA 2024 · 被引用 16 次
- Asparagus: Automated Synthesis of Parametric Gas Upper-Bounds for Smart ContractsZhuo Cai, Soroush Farokhnia, Amir Kafshdar Goharshady, S. HitarthOOPSLA 2023 · 被引用 16 次
- Algebro-geometric Algorithms for Template-Based Synthesis of Polynomial ProgramsAmir Kafshdar Goharshady, S. Hitarth, Fatemeh Mohammadi, Harshit J. MotwaniOOPSLA 2023 · 被引用 15 次
它引用的顶会 Paper2
相关 Paper
- Sound and Complete Witnesses for Template-Based Verification of LTL Properties on Polynomial ProgramsKrishnendu Chatterjee, Amir Kafshdar Goharshady, Ehsan Kafshdar Goharshady, Mehrdad Karrabi 等FM 2024 · 被引用 11 次
- Scalable linear invariant generation with Farkas' lemmaHongming Liu, Hongfei Fu, Zhiyong Yu, Jiaxin Song 等OOPSLA 2022 · 被引用 15 次
- Simple Linear Loops: Algebraic Invariants and ApplicationsRida Ait El Manssour, George Kenison, Mahsa Shirmohammadi, Anton VaronkaPOPL 2025 · 被引用 2 次
- Breaking the Mold: Nonlinear Ranking Function Synthesis Without TemplatesShaowei Zhu, Zachary KincaidCAV 2024 · 被引用 2 次
- Strong Invariants Are Hard: On the Hardness of Strongest Polynomial Invariants for (Probabilistic) ProgramsJulian Müllner, Marcel Moosbrugger, Laura KovácsPOPL 2024 · 被引用 7 次
