Apron

Apron: a library of numerical abstract domains for static analysis. This article describes Apron, a freely available library dedicated to the static analysis of the numerical variables of programs by abstract interpretation. Its goal is threefold: provide analysis implementers with ready-to-use numerical abstractions under a unified API, encourage the research in numerical abstract domains by providing a platform for integration and comparison, and provide teaching and demonstration tools to disseminate knowledge on abstract interpretation.


References in zbMATH (referenced in 55 articles )

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  1. Howe, Jacob M.; King, Andy; Simon, Axel: Incremental closure for systems of two variables per inequality (2019)
  2. Boutonnet, Rémy; Halbwachs, Nicolas: Improving the results of program analysis by abstract interpretation beyond the decreasing sequence (2018)
  3. Heo, Kihong; Oh, Hakjoo; Yang, Hongseok: Learning analysis strategies for octagon and context sensitivity from labeled data generated by static analyses (2018)
  4. Blazy, Sandrine; Bühler, David; Yakobowski, Boris: Structuring abstract interpreters through state and value abstractions (2017)
  5. Botbol, Vincent; Chailloux, Emmanuel; Le Gall, Tristan: Static analysis of communicating processes using symbolic transducers (2017)
  6. Dan, Andrei; Meshman, Yuri; Vechev, Martin; Yahav, Eran: Effective abstractions for verification under relaxed memory models (2017)
  7. Jiang, Jiahong; Chen, Liqian; Wu, Xueguang; Wang, Ji: Block-wise abstract interpretation by combining abstract domains with SMT (2017)
  8. Liu, Jiangchao; Rival, Xavier: An array content static analysis based on non-contiguous partitions (2017)
  9. Magron, Victor; Constantinides, George; Donaldson, Alastair: Certified roundoff error bounds using semidefinite programming (2017)
  10. Maréchal, Alexandre; Périn, Michaël: Efficient elimination of redundancies in polyhedra by raytracing (2017)
  11. Monat, Raphaël; Miné, Antoine: Precise thread-modular abstract interpretation of concurrent programs using relational interference abstractions (2017)
  12. Singh, Gagandeep; Püschel, Markus; Vechev, Martin: Fast polyhedra abstract domain (2017)
  13. Urban, Caterina; Miné, Antoine: Inference of ranking functions for proving temporal properties by abstract interpretation (2017)
  14. Frohn, F.; Naaf, M.; Hensel, J.; Brockschmidt, M.; Giesl, J.: Lower runtime bounds for integer programs (2016)
  15. Maréchal, Alexandre; Fouilhé, Alexis; King, Tim; Monniaux, David; Périn, Michael: Polyhedral approximation of multivariate polynomials using handelman’s theorem (2016)
  16. D’Silva, Vijay; Urban, Caterina: Conflict-driven conditional termination (2015)
  17. Goubault, Eric; Putot, Sylvie: A zonotopic framework for functional abstractions (2015)
  18. Brain, Martin; D’Silva, Vijay; Griggio, Alberto; Haller, Leopold; Kroening, Daniel: Deciding floating-point logic with abstract conflict driven clause learning (2014)
  19. Cox, Arlen; Chang, Bor-Yuh Evan; Sankaranarayanan, Sriram: Quicr: a reusable library for parametric abstraction of sets and numbers (2014)
  20. Wu, Xueguang; Chen, Liqian; Wang, Ji: An abstract domain to infer symbolic ranges over nonnegative parameters (2014)

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