Skip to main content
SpringerLink
Log in

A Novel Approach to String Constraint Solving

  • Conference paper
  • First Online:
Principles and Practice of Constraint Programming (CP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

String processing is ubiquitous across computer science, and arguably more so in web programming. In order to reason about programs manipulating strings we need to solve constraints over strings. In Constraint Programming, the only approaches we are aware for representing string variables—having bounded yet possibly unknown size—degrade when the maximum possible string length becomes too large. In this paper, we introduce a novel approach that decouples the size of the string representation from its maximum length. The domain of a string variable is dynamically represented by a simplified regular expression that we called a dashed string, and the constraint solving relies on propagation of information based on equations between dashed strings. We implemented this approach in G-Strings, a new string solver—built on top of Gecode solver—that already shows some promising results.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For conciseness, for integer variable x, we define \(\underline{x} = \min (\mathcal {D}(x))\) and \(\overline{x} = \max (\mathcal {D}(x))\).

  2. 2.

    We used the last stable release: https://sites.google.com/site/z3strsolver/.

References

  1. Amadini, R., Flener, P., Pearson, J., Scott, J.D., Stuckey, P.J., Tack, G.: Minizinc with strings. In: Logic-Based Program Synthesis and Transformation - 25th International Symposium, LOPSTR 2016 (2016). https://arxiv.org/abs/1608.03650

  2. Amadini, R., Gabbrielli, M., Mauro, J.: A multicore tool for constraint solving. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 232–238. AAAI Press (2015)

    Google Scholar 

  3. Amadini, R., Jordan, A., Gange, G., Gauthier, F., Schachte, P., Søndergaard, H., Stuckey, P.J., Zhang, C.: Combining string abstract domains for javascript analysis: an evaluation. In: Legay, A., Margaria, T. (eds.) TACAS 2017. LNCS, vol. 10205, pp. 41–57. Springer, Heidelberg (2017). doi:10.1007/978-3-662-54577-5_3

    Chapter  Google Scholar 

  4. Barahona, P., Krippahl, L.: Constraint programming in structural bioinformatics. Constraints 13(1–2), 3–20 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bisht, P., Hinrichs, T.L., Skrupsky, N., Venkatakrishnan, V.N.: WAPTEC: whitebox analysis of web applications for parameter tampering exploit construction. In: Proceedings of ACM Conference on Computer and Communications Security, pp. 575–586. ACM (2011)

    Google Scholar 

  6. Björdal, G.: String variables for constraint-based local search. Master’s thesis, Department of Information Technology, Uppsala University, Sweden, August 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301501

  7. Björdal, G., Monette, J.-N., Flener, P., Pearson, J.: A constraint-based local search backend for MiniZinc. Constraints 20(3), 325–345 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bjørner, N., Tillmann, N., Voronkov, A.: Path feasibility analysis for string-manipulating programs. In: Kowalewski, S., Philippou, A. (eds.) TACAS 2009. LNCS, vol. 5505, pp. 307–321. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00768-2_27

    Chapter  Google Scholar 

  9. Chu, G.: Improving combinatorial optimization. Ph.D. thesis, Department of Computing and Information Systems, University of Melbourne, Australia (2011)

    Google Scholar 

  10. Costantini, G., Ferrara, P., Cortesi, A.: A suite of abstract domains for static analysis of string values. Softw.: Pract. Exp. 45(2), 245–287 (2015)

    Google Scholar 

  11. Cousot, P., Cousot, R.: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the Fourth ACM Symposium on Principles of Programming Languages, pp. 238–252. ACM (1977)

    Google Scholar 

  12. Emmi, M., Majumdar, R., Sen, K.: Dynamic test input generation for database applications. In: Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), pp. 151–162. ACM (2007)

    Google Scholar 

  13. Feydy, T., Somogyi, Z., Stuckey, P.J.: Half reification and flattening. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 286–301. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23786-7_23

    Chapter  Google Scholar 

  14. Fu, X., Powell, M.C., Bantegui, M., Li, C.: Simple linear string constraints. Form. Asp. Comput. 25(6), 847–891 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Fujiwara, T.: iZplus description (2016). http://www.minizinc.org/challenge2016/description_izplus.txt

  16. Ganesh, V., Minnes, M., Solar-Lezama, A., Rinard, M.: Word equations with length constraints: what’s decidable? In: Biere, A., Nahir, A., Vos, T. (eds.) HVC 2012. LNCS, vol. 7857, pp. 209–226. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39611-3_21

    Chapter  Google Scholar 

  17. Gange, G., Navas, J.A., Stuckey, P.J., Søndergaard, H., Schachte, P.: Unbounded model-checking with interpolation for regular language constraints. In: Piterman, N., Smolka, S.A. (eds.) TACAS 2013. LNCS, vol. 7795, pp. 277–291. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36742-7_20

    Chapter  Google Scholar 

  18. Gecode Team. Gecode: generic constraint development environment (2016). http://www.gecode.org

  19. Golden, K., Pang, W.: Constraint reasoning over strings. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 377–391. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45193-8_26

    Chapter  Google Scholar 

  20. He, J., Flener, P., Pearson, J., Zhang, W.M.: Solving string constraints: the case for constraint programming. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 381–397. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40627-0_31

    Chapter  Google Scholar 

  21. Hooimeijer, P., Weimer, W.: StrSolve: solving string constraints lazily. Autom. Softw. Eng. 19(4), 531–559 (2012)

    Article  Google Scholar 

  22. Kiezun, A., Ganesh, V., Artzi, S., Guo, P.J., Hooimeijer, P., Ernst, M.D.: HAMPI: a solver for word equations over strings, regular expressions, and context-free grammars. ACM Trans. Softw. Eng. Methodol. 21(4), Article 25 (2012)

    Google Scholar 

  23. Kim, S.-W., Chin, W., Park, J., Kim, J., Ryu, S.: Inferring grammatical summaries of string values. In: Garrigue, J. (ed.) APLAS 2014. LNCS, vol. 8858, pp. 372–391. Springer, Cham (2014). doi:10.1007/978-3-319-12736-1_20

    Google Scholar 

  24. Li, G., Ghosh, I.: PASS: string solving with parameterized array and interval automaton. In: Bertacco, V., Legay, A. (eds.) HVC 2013. LNCS, vol. 8244, pp. 15–31. Springer, Cham (2013). doi:10.1007/978-3-319-03077-7_2

    Chapter  Google Scholar 

  25. Madsen, M., Andreasen, E.: String analysis for dynamic field access. In: Cohen, A. (ed.) CC 2014. LNCS, vol. 8409, pp. 197–217. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54807-9_12

    Chapter  Google Scholar 

  26. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74970-7_38

    Chapter  Google Scholar 

  27. Ohrimenko, O., Stuckey, P.J., Codish, M.: Propagation via lazy clause generation. Constraints 14(3), 357–391 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  28. Saxena, P., Akhawe, D., Hanna, S., Mao, F., McCamant, S., Song, D.: A symbolic execution framework for JavaScript. In: S&P, pp. 513–528. IEEE Computer Society (2010)

    Google Scholar 

  29. Scott, J.D.: Other things besides number: abstraction, constraint propagation, and string variable types. Ph.D. thesis, Department of Information Technology, Uppsala University, Sweden (2016). http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273311

  30. Scott, J.D., Flener, P., Pearson, J.: Constraint solving on bounded string variables. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 375–392. Springer, Cham (2015). doi:10.1007/978-3-319-18008-3_26

    Google Scholar 

  31. Scott, J.D., Flener, P., Pearson, J., Schulte, C.: Design and implementation of bounded-length sequence variables. In: Salvagnin, D., Lombardi, M. (eds.) CPAIOR 2017. LNCS, vol. 10335, pp. 51–67. Springer, Cham (2017). doi:10.1007/978-3-319-59776-8_5

    Chapter  Google Scholar 

  32. Tateishi, T., Pistoia, M., Tripp, O.: Path- and index-sensitive string analysis based on monadic second-order logic. ACM Trans. Softw. Eng. Methodol. 22(4), 33 (2013)

    Article  Google Scholar 

  33. Yu, F., Alkhalaf, M., Bultan, T.: Stranger: an automata-based string analysis tool for PHP. In: Esparza, J., Majumdar, R. (eds.) TACAS 2010. LNCS, vol. 6015, pp. 154–157. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12002-2_13

    Chapter  Google Scholar 

  34. Zheng, Y., Ganesh, V., Subramanian, S., Tripp, O., Dolby, J., Zhang, X.: Effective search-space pruning for solvers of string equations, regular expressions and length constraints. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 235–254. Springer, Cham (2015). doi:10.1007/978-3-319-21690-4_14

    Chapter  Google Scholar 

Download references

Acknowledgements

This work is supported by the Australian Research Council (ARC) through Linkage Project Grant LP140100437 and Discovery Early Career Researcher Award DE160100568.

Author information

Authors and Affiliations

  1. University of Melbourne, Melbourne, Victoria, Australia

    Roberto Amadini, Graeme Gange & Peter J. Stuckey

  2. Monash University, Melbourne, Australia

    Guido Tack

Authors
  1. Roberto Amadini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graeme Gange
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter J. Stuckey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guido Tack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roberto Amadini .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, Ontario, Canada

    J. Christopher Beck

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Amadini, R., Gange, G., Stuckey, P.J., Tack, G. (2017). A Novel Approach to String Constraint Solving. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66158-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66157-5

  • Online ISBN: 978-3-319-66158-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation