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Compositional Safety LTL Synthesis

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Verified Software. Theories, Tools and Experiments. (VSTTE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13800))

Abstract

Reactive synthesis holds the promise of generating automatically a verifiably correct program from a high-level specification. A popular such specification language is Linear Temporal Logic (LTL). Unfortunately, synthesizing programs from general LTL formulas, which relies on first constructing a game arena and then solving the game, does not scale to large instances. The specifications from practical applications are usually large conjunctions of smaller LTL formulas, which inspires existing compositional synthesis approaches to take advantage of this structural information. The main challenge here is that they solve the game only after obtaining the game arena, the most computationally expensive part in the procedure. In this work, we propose a compositional synthesis technique to tackle this difficulty by synthesizing a program for each small conjunct separately and composing them one by one. While this approach does not work for general LTL formulas, we show here that it does work for Safety LTL formulas, a popular and important fragment of LTL. While we have to compose all the programs of small conjuncts in the worst case, we can prune the intermediate programs to make later compositions easier and immediately conclude unrealizable as soon as some part of the specification is found unrealizable. By comparing our compositional approach with a portfolio of all other approaches, we observed that our approach was able to solve a notable number of instances not solved by others. In particular, experiments on scalable conjunctive benchmarks showed that our approach scale well and significantly outperform current Safety LTL synthesis techniques. We conclude that our compositional approach is an important contribution to the algorithmic portfolio of Safety LTL synthesis.

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Acknowledgement

This work is supported in part by the ERC Advanced Grant WhiteMech (No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215), the PRIN project RIPER (No. 20203FFYLK), the National Natural Science Foundation of China (Grant Nos. 62102407 and 61836005), CAS grant QYZDB-SSW-SYS019, NSF grants IIS-1527668, CCF-1704883, IIS-1830549, CNS-2016656, DoD MURI grant N00014-20-1-2787, and an award from the Maryland Procurement Office.

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Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Suguman Bansal

  2. Sapienza University of Rome, Rome, Italy

    Giuseppe De Giacomo, Antonio Di Stasio & Shufang Zhu

  3. SKLCS, Institute of Software, CAS, Beijing, China

    Yong Li

  4. Rice University, Houston, TX, USA

    Moshe Y. Vardi

Authors
  1. Suguman Bansal
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  2. Giuseppe De Giacomo
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  3. Antonio Di Stasio
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  4. Yong Li
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  5. Moshe Y. Vardi
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  6. Shufang Zhu
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Corresponding author

Correspondence to Shufang Zhu .

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Editors and Affiliations

  1. Microsoft Research, Karnataka, India

    Akash Lal

  2. Fondazione Bruno Kessler, Trento, Italy

    Stefano Tonetta

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Bansal, S., De Giacomo, G., Di Stasio, A., Li, Y., Vardi, M.Y., Zhu, S. (2023). Compositional Safety LTL Synthesis. In: Lal, A., Tonetta, S. (eds) Verified Software. Theories, Tools and Experiments.. VSTTE 2022. Lecture Notes in Computer Science, vol 13800. Springer, Cham. https://doi.org/10.1007/978-3-031-25803-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-25803-9_1

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