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Synthesizing Programs from Program Pieces Using Genetic Programming and Refinement Type Checking

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Genetic Programming (EuroGP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13223))

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Abstract

Program synthesis automates the process of writing code, which can be a very useful tool in allowing people to better leverage computational resources. However, a limiting factor in the scalability of current program synthesis techniques is the large size of the search space, especially for complex programs. We present a new model for synthesizing programs which reduces the search space by composing programs from program pieces, which are component functions provided by the user. Our method uses genetic programming search with a fitness function based on refinement type checking, which is a formal verification method that checks function behavior expressed through types. We evaluate our implementation of this method on a set of 3 benchmark problems, observing that our fitness function is able to find solutions in fewer generations than a fitness function that uses example test cases. These results indicate that using refinement types and other formal methods within genetic programming can improve the performance and practicality of program synthesis.

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Notes

  1. 1.

    https://github.com/sabrinatseng/GAble.

References

  1. Bland, J.M., Altman, D.G.: Multiple significance tests: the Bonferroni method. BMJ 310(6973), 170 (1995). http://bmj.bmjjournals.com/cgi/content/full/310/6973/170

  2. Cochran, R.A., D’Antoni, L., Livshits, B., Molnar, D., Veanes, M.: Program boosting: program synthesis via crowd-sourcing. SIGPLAN Not. 50(1), 677–688 (2015). https://doi.org/10.1145/2775051.2676973

    Article  MATH  Google Scholar 

  3. David, C., Kroening, D.: Program synthesis: challenges and opportunities. Philos. Trans. Ser. A Math. Phys. Eng. Sci. 375(2104), Article ID 20150403 (2017)

    Google Scholar 

  4. Fang, Y., Li, J.: A review of tournament selection in genetic programming. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 181–192. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16493-4_19

    Chapter  Google Scholar 

  5. Fonseca, A., Santos, P., Silva, S.: The usability argument for refinement typed genetic programming. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 18–32. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58115-2_2

    Chapter  Google Scholar 

  6. Giacobini, M., Tomassini, M., Vanneschi, L.: Limiting the number of fitness cases in genetic programming using statistics. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 371–380. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45712-7_36

    Chapter  Google Scholar 

  7. Gulwani, S., Polozov, O., Singh, R.: Program synthesis. Found. Trends® Program. Lang. 4(1–2), 1–119 (2017). https://doi.org/10.1561/2500000010

    Article  Google Scholar 

  8. He, P., Kang, L., Johnson, C.G., Ying, S.: Hoare logic-based genetic programming. Sci. China Inf. Sci. 54(3), 623–637 (2011). https://doi.org/10.1007/s11432-011-4200-4

    Article  MathSciNet  MATH  Google Scholar 

  9. Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, pp. 1039–1046. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2739480.2754769

  10. Hemberg, E., Kelly, J., O’Reilly, U.M.: On domain knowledge and novelty to improve program synthesis performance with grammatical evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 1039–1046. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3321707.3321865

  11. Hudak, P., et al.: Report on the programming language Haskell: a non-strict, purely functional language version 1.2. SIGPLAN Not. 27(5), 1–164 (1992). https://doi.org/10.1145/130697.130699

    Article  Google Scholar 

  12. Johnson, C.G.: Genetic programming with fitness based on model checking. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 114–124. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71605-1_11

    Chapter  Google Scholar 

  13. Kitzelmann, E.: Inductive programming: a survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11931-6_3

    Chapter  Google Scholar 

  14. Koza, J.R.: Survey of genetic algorithms and genetic programming. In: Proceedings of WESCON 1995, pp. 589– (1995)

    Google Scholar 

  15. Krawiec, K.: Behavioral Program Synthesis with Genetic Programming. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27565-9

    Book  Google Scholar 

  16. Krawiec, K., O’Reilly, U.M.: Behavioral programming: a broader and more detailed take on semantic GP. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 935–942. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2576768.2598288

  17. Mandal, S., Anderson, T.A., Turek, J.S., Gottschlich, J., Zhou, S., Muzahid, A.: Learning fitness functions for machine programming (2021)

    Google Scholar 

  18. Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947). https://doi.org/10.1214/aoms/1177730491

    Article  MathSciNet  MATH  Google Scholar 

  19. McKay, R.I.B.: Fitness sharing in genetic programming. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, GECCO 2000, San Francisco, CA, USA, pp. 435–442. Morgan Kaufmann Publishers Inc. (2000)

    Google Scholar 

  20. de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24

    Chapter  Google Scholar 

  21. O’Neill, M., Spector, L.: Automatic programming: the open issue? Genet. Program Evolvable Mach. 21, 251–262 (2019). https://doi.org/10.1007/s10710-019-09364-2

    Article  Google Scholar 

  22. O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genet. Program Evolvable Mach. 11(3–4), 339–363 (2010). https://doi.org/10.1007/s10710-010-9113-2

    Article  Google Scholar 

  23. Page, J., Poli, R., Langdon, W.B.: Mutation in genetic programming: a preliminary study. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 39–48. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48885-5_4

    Chapter  Google Scholar 

  24. Poli, R., Langdon, W.B.: Genetic programming with one-point crossover. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 180–189. Springer, London (1998). https://doi.org/10.1007/978-1-4471-0427-8_20

    Chapter  Google Scholar 

  25. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. (2008)

    Google Scholar 

  26. Poli, R., McPhee, N.F., Vanneschi, L.: Elitism reduces bloat in genetic programming. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1343–1344. Association for Computing Machinery, New York (2008). https://doi.org/10.1145/1389095.1389355

  27. Polikarpova, N., Kuraj, I., Solar-Lezama, A.: Program synthesis from polymorphic refinement types. SIGPLAN Not. 51(6), 522–538 (2016). https://doi.org/10.1145/2980983.2908093

    Article  Google Scholar 

  28. Rondon, P.M., Kawaguci, M., Jhala, R.: Liquid types. In: Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2008, pp. 159–169. Association for Computing Machinery, New York (2008). https://doi.org/10.1145/1375581.1375602

  29. Solar-Lezama, A.: Program synthesis by sketching. Ph.D. thesis, University of California at Berkeley, USA (2008)

    Google Scholar 

  30. Solar-Lezama, A., Jones, C.G., Bodik, R.: Sketching concurrent data structures. SIGPLAN Not. 43(6), 136–148 (2008). https://doi.org/10.1145/1379022.1375599

    Article  Google Scholar 

  31. Solar-Lezama, A., Tancau, L., Bodik, R., Seshia, S., Saraswat, V.: Combinatorial sketching for finite programs. SIGARCH Comput. Archit. News 34(5), 404–415 (2006). https://doi.org/10.1145/1168919.1168907

    Article  Google Scholar 

  32. Vazou, N., Rondon, P.M., Jhala, R.: Abstract refinement types. In: Felleisen, M., Gardner, P. (eds.) ESOP 2013. LNCS, vol. 7792, pp. 209–228. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37036-6_13

    Chapter  Google Scholar 

  33. Vazou, N., Seidel, E.L., Jhala, R.: LiquidHaskell: experience with refinement types in the real world. In: Proceedings of the 2014 ACM SIGPLAN Symposium on Haskell, Haskell 2014, pp. 39–51. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2633357.2633366

  34. Vazou, N., Seidel, E.L., Jhala, R., Vytiniotis, D., Peyton-Jones, S.: Refinement types for Haskell. SIGPLAN Not. 49(9), 269–282 (2014). https://doi.org/10.1145/2692915.2628161

    Article  MathSciNet  MATH  Google Scholar 

  35. Yampolskiy, R.V.: AI-complete, AI-hard, or AI-easy - classification of problems in AI. In: MAICS (2012)

    Google Scholar 

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Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Sabrina Tseng, Erik Hemberg & Una-May O’Reilly

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  1. Sabrina Tseng
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  2. Erik Hemberg
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  3. Una-May O’Reilly
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Corresponding author

Correspondence to Sabrina Tseng .

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Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Eric Medvet

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  3. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

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Tseng, S., Hemberg, E., O’Reilly, UM. (2022). Synthesizing Programs from Program Pieces Using Genetic Programming and Refinement Type Checking. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-02056-8_13

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