Two-layered falsification of hybrid systems guided by Monte Carlo tree search

Zhenya Zhang, Gidon Ernst, Sean Sedwards, Paolo Arcaini, Ichiro Hasuo

研究成果: Contribution to journalArticle査読

17 被引用数 (Scopus)

抄録

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification- A methodology of search-based testing that employs stochastic optimization-is thus attracting attention as an alternative quality assurance method. Inspired by the recent work that advocates coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations (e.g., in computer Go). MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner. We demonstrate the proposed framework through experiments with benchmarks from the automotive domain.

本文言語英語
論文番号8418450
ページ(範囲)2894-2905
ページ数12
ジャーナルIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
37
11
DOI
出版ステータス出版済み - 11 2018
外部発表はい

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学

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