Skip to main content
SpringerLink
Log in

An approach based on knowledge exploration for state space management in checking reachability of complex software systems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Model checking is one of the most efficient techniques in software system verification. However, state space explosion is a big challenge while using this technique to check different properties like safety ones. In this situation, one can search the state space to find a reachable state in which the safety property is violated. Hence, reachability checking can be done instead of checking safety property. However, checking reachability in the worst case may cause state space explosion again. To handle this problem, our idea is based on generating a small model consistent with the main model. Then by exploring the state space entirely, we search it to find the goal states. After finding the goal states, we label the paths which start from the initial state and leading to a goal state. Then using the ensemble classification technique, the necessary knowledge is extracted from these paths to intelligently explore the state space of the bigger model. Ensemble machine learning technique uses Boosting method along with decision trees. It follows sampling techniques by replacement. This method generates k predictive models after sampling k times. Finally, it uses a voting mechanism to predict the labels of the final path. Our proposed approach is implemented in GROOVE, which is an open source toolset for designing and model checking graph transformation systems. Our experiments show a significant improvement in terms of both speed and memory usage. In average, our approach consumes nearly 42% fewer memory than other approaches. Also, it generates witnesses nearly 20% shorter than others, in average.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alba E, Chicano F (2007) Finding safety errors with ACO. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, pp. 1066–1073

  • Alba E, Chicano F, Ferreira M, Gomez-Pulido J (2008) Finding deadlocks in large concurrent java programs using genetic algorithms. In: 10th annual conference on genetic and evolutionary computation, pp 1735–1742

  • Allen R, Garlan D (1997) A formal basis for architectural connection. ACM Trans Softw Eng Methodol (TOSEM) 6(3):213–249

    Article  Google Scholar 

  • Alpaydin E (2011) Introduction to machine learning, 2nd edn. MIT Press, Cambridge

    MATH  Google Scholar 

  • Arcuri A, Briand L (2011) A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: 33rd international conference software engineering (ICSE). IEEE, pp 1–10

  • Behjati R, Sirjani M, Ahmadabadi MN (2010) Bounded rational search for on-the-fly model checking of LTL properties. Fundam Softw Eng 5961:292–307

    Article  Google Scholar 

  • Boneva I, Kreiker J, Kurban M, Rensink A, Zambon E (2012) Graph abstraction and abstract graph transformation (amended version). Technical Report TR-CTIT-12-26, University of Twente, October 2012

  • Castro SJB, Crespo RG, García VHM (2011) Patterns of software development process. Int J Interact Multimed Artif Intell 1(4):33–40

    Google Scholar 

  • Clarke EM, McMillan KL, Campos SVA, Hartonas-Garmhausen VI (1996) Symbolic model checking. Comput Aided Verif 1102:419–422

    Article  Google Scholar 

  • Duarte LM, Foss L, Wagner R, Heimfarth T (2010) Model checking the ant colony optimization. In: Distributed, parallel and biologically inspired systems. IFIP advances in information and communication technology, vol 329, pp 221–232

  • Edelkamp S, Reffel F (1998) OBDDs in heuristic search. In: Annual conference on artificial intelligence. Springer, Berlin, pp 81–92

  • Edelkamp S, Lafuente AL, Leue S (2001) Protocol verification with heuristic search. In: AAAI symposium on model based validation of intelligence

  • Edelkamp S, Lafuente AL, Leue S (2001) Protocol verification with heuristic search. In: AAAI symposium on model-based validation of intelligence, pp 75–83

  • Edelkamp S, Leue S, Lafuente AL (2004) Directed explicit-state model checking in the validation of communication protocols. Int J Softw Tools Technol Transf (STTT) 5(2–3):247–267

    Article  Google Scholar 

  • Elsinga JW (2016) On a framework for domain independent heuristics in graph transformation planning. Master’s thesis, University of Twente

  • Engels G, Hausmann JH, Heckel R, Sauer S (2000) Dynamic meta modeling: a graphical approach to the operational semantics of behavioral diagrams in UML. In: 3rd international conference on the unified modeling language: advancing the standard (UML’00). Springer, pp. 323–337

  • Estler HC, Wehrheim H (2011) Heuristic search-based planning for graph transformation systems. In: KEPS 2011, p 54

  • Francesca G, Santone A, Vaglini G, Villani ML (2011) Ant colony optimization for deadlock detection in concurrent systems. IEEE Computer Society, pp 108–117

  • Garlan D, Allen R, Ockerbloom J (1994) Exploiting style in architectural design environments. In ACM SIGSOFT software engineering notes, vol 19, no 5. ACM, pp 175–188

  • Godefroid P, Khurshid S (2002) Exploring very large state spaces using genetic algorithms. In: International conference on tools and algorithms for the construction and analysis of systems. Springer, Berlin, pp 266–280

  • Groce A, Visser W (2004) Heuristics for model checking Java programs. Int J Softw Tools Technol Transf (STTT) 6(4):260–276

    Article  Google Scholar 

  • Gyuris V, Sistla A (1999) On-the-fly model checking under fairness that exploits symmetry. Form Methods Syst Des 15:217–238

    Article  Google Scholar 

  • Habiboghli A, Jalali T (2016) A solution to the N-queens problem using biogeography-based optimization. Int J Interact Multimed Artif Intell 4(4):20–26

    Google Scholar 

  • He X, Ma Z, Shao W, Li G (2007) A meta model for the notation of graphical modeling languages. In: 31st annual international computer software and applications conference, COMPSAC 2007, vol 1. IEEE, pp 219–224

  • Heckel R (2006) Graph transformation in a Nutshell. Electron Notes Theor Comput Sci (ENTCS) 148:187–198

    Article  Google Scholar 

  • Heckel R, Baresi L (2002) Tutorial introduction to graph transformation: a software engineering perspective. In: First international conference on graph transformation (ICGT), pp 402–429

  • i Casas PF, Pi X, Casanovas J, Jové J (2013) Definition of virtual reality simulation models using specification and description language diagrams. In: International SDL Forum. Springer, Berlin, pp 258–274

  • Kastenberg H, Rensink A (2006) Model checking dynamic states in GROOVE. In: Of the 13th international conference on model checking software. Springer-Verlag, Berlin, pp 299–305

  • Khari M, Kumar P, Burgos D, Crespo RG (2018) Optimized test suites for automated testing using different optimization techniques. Soft Comput 22(24):8341–8352

    Article  Google Scholar 

  • Lafuente AL (2003) Symmetry reduction and heuristic search for error detection in model checking. In: Workshop on model checking and artificial intelligence

  • Maeoka J, Tanabe Y, Ishikawa F (2016) Depth-first heuristic search for software model checking. In: Lee R (ed) Computer and information science. Springer, Berlin, pp 75–96

    Google Scholar 

  • Meza J, Espitia H, Montenegro C, Crespo RG (2016) Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior. Soft Comput 20(9):3521–3536

    Article  Google Scholar 

  • Peng H, Tahar S (1998) A survey on compositional verification. J Circuits Syst Comput 8:16–25

    Google Scholar 

  • Pira E, Rafe V, Nikanjam A (2016) EMCDM: efficient model checking by data mining for verification of complex software systems specified through architectural styles. Appl Soft Comput 44:1185–1201

    Article  Google Scholar 

  • Pira E, Rafe V, Nikanjam A (2017) Deadlock detection in complex software systems specified through graph transformation using Bayesian optimization algorithm. J Syst Softw 131:181–200

    Article  Google Scholar 

  • Rafe V (2013) Scenario-driven analysis of systems specified through graph transformations. J Vis Lang Comput 24(2):136–145

    Article  Google Scholar 

  • Rafe V, Moradi M, Yousefian R, Nikanjam A (2015) a meta-heuristic approach for automated refutation of complex software systems specified through graph transformations. Appl Soft Comput 33:136–149

    Article  Google Scholar 

  • Rahmani AT, Rafe V (2009) Towards automated software model checking using graph transformation systems and Bogor. Zhejiang Univ Sci A 10:1093–1105

    Article  Google Scholar 

  • Rensink A, Zambon E (2012) Pattern-based graph abstraction in graph transformations. Springer, Berlin, pp 66–80

    Book  Google Scholar 

  • Rozenberg G (1997) Handbook of graph grammars and computing by graph transformation, vol 1. World Scientific, Singapore

    Book  Google Scholar 

  • Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining prediction. Morgan and Claypool, San Rafael

    Google Scholar 

  • Serengil SI, Ozpinar A (2018) Workforce optimization for bank operation centers: a machine learning approach. Int J Interact Multimed Artif Intell 4(6):81–87

    Google Scholar 

  • Snippe E (2011) Using heuristic search to solve planning problems in GROOVE. In: 14th Twente student conference on IT, University of Twente

  • Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, Cambridge

    MATH  Google Scholar 

  • Thöne S (2005) Dynamic software architectures: a style based modeling and refinement technique with graph transformations. Ph.D. Thesis, Faculty of Computer Science, Electrical Engineering, and Mathematics, University of Paderborn

  • Webster BL (2004) Solving combinatorial optimization problems using a new algorithm based on gravitational attraction. Florida Institute of Technology

  • Wolper P, Godefroid P (1991) Using partial orders for the efficient verification of deadlock freedom and safety properties. Formal Methods in System Design—Special issue on computer-aided verification: special methods II, pp 149–164

  • Yousefian R, Rafe V, Rahmani M (2014) A heuristic approach for model checking graph transformation systems. Appl Soft Comput 24:169–180

    Article  Google Scholar 

  • Yousefian R, Aboutorabi S, Rafe V (2016) A greedy algorithm versus metaheuristic solutions to deadlock detection in graph transformation systems. J Intell Fuzzy Syst 13(1):1–13

    Google Scholar 

  • Ziegert S (2014) Graph transformation planning via abstraction. arXiv preprint arXiv:1407.7933

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Jaafar Partabian

  2. Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran

    Vahid Rafe

  3. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad, Mamasani, Iran

    Hamid Parvin

  4. Young Researchers and Elite Club Nourabad Mamasani Branch, Islamic Azad University, Nourabad, Mamasani, Iran

    Hamid Parvin

  5. Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian

  6. Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian

Authors
  1. Jaafar Partabian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahid Rafe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Parvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samad Nejatian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahid Rafe.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest for any of them in this article.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Partabian, J., Rafe, V., Parvin, H. et al. An approach based on knowledge exploration for state space management in checking reachability of complex software systems. Soft Comput 24, 7181–7196 (2020). https://doi.org/10.1007/s00500-019-04334-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04334-3

Keywords

Search

Navigation