skip to main content
10.1145/2908961.2931690acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Speeding up the Proof Strategy in Formal Software Verification

Published:20 July 2016Publication History

ABSTRACT

The functional correctness of safety- and security-critical software is of utmost importance. Nowadays, this can be achieved through computer assisted verification.

While formal verification itself typically poses a steep learning-curve for anyone who wants to apply it, its applicability is further hindered by its (typically) low runtime performance.

With the increasing popularity of algorithm parameter tuning and genetic improvement, we see a great opportunity for assisting verification engineers in their daily tasks.

References

  1. B. Beckert, R. Hähnle, and P. H. Schmitt, editors. Verification of Object-Oriented Software: The KeY Approach. Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. Beckert, T. Bormer, and M. Wagner. Heuristically creating test cases for program verification systems. In Metaheuristics International Conference (MIC), 2013.Google ScholarGoogle Scholar
  3. B. Bérard, M. Bidoit, A. Finkel, F. Laroussinie, A. Petit, L. Petrucci, and P. Schnoebelen. Systems and software verification: model-checking techniques and tools. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. A. Bokhari, T. Bormer, and M. Wagner. 7th International Symposium on Search-Based Software Engineering (SSBSE), chapter An Improved Beam-Search for the Test Case Generation for Formal Verification Systems, pages 77--92. Springer, 2015.Google ScholarGoogle Scholar
  5. B. R. Bruce, J. Petke, and M. Harman. Reducing energy consumption using genetic improvement. In Genetic and Evolutionary Computation (GECCO), pages 1327--1334. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. E. W. Dijkstra. Guarded commands, nondeterminacy and formal derivation of programs. Communications of the ACM, 18 (8): 453--457, 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. F. Hutter, D. Babic, H. H. Hoos, and A. J. Hu. Boosting verification by automatic tuning of decision procedures. In Formal Methods in Computer Aided Design (FMCAD), pages 27--34, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. F. Hutter, H. H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization (LION), pages 507--523, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Wagner. Maximising axiomatization coverage and minimizing regression testing time. In IEEE Congress on Evolutionary Computation (CEC), pages 2885--2892, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. F. Wu, W. Weimer, M. Harman, Y. Jia, and J. Krinke. Deep parameter optimisation. In Genetic and Evolutionary Computation Conference (GECCO), pages 1375--1382. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Speeding up the Proof Strategy in Formal Software Verification

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
        July 2016
        1510 pages
        ISBN:9781450343237
        DOI:10.1145/2908961

        Copyright © 2016 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 20 July 2016

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        GECCO '16 Companion Paper Acceptance Rate137of381submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

        Upcoming Conference

        GECCO '24
        Genetic and Evolutionary Computation Conference
        July 14 - 18, 2024
        Melbourne , VIC , Australia

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader