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Static Analysis of Data Science Software

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 11822)

Abstract

Data science software is playing an increasingly important role in every aspect of our daily lives and is even slowly creeping into mission critical scenarios, despite being often opaque and unpredictable. In this paper, we will discuss some key challenges and a number of research questions that we are currently addressing in developing static analysis methods and tools for data science software.

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  • DOI: 10.1007/978-3-030-32304-2_2
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Notes

  1. 1.

    https://www.airbus.com/innovation/future-technology/artificial-intelligence.html.

  2. 2.

    http://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai.

  3. 3.

    https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-sentencing-bias.html.

  4. 4.

    https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.

  5. 5.

    https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html.

  6. 6.

    https://www.tesla.com/blog/tragic-loss.

References

  1. Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.V.: FairSquare: probabilistic verification of program fairness. In: PACMPL, vol. 1(OOPSLA), pp. 80:1–80:30 (2017)

    CrossRef  Google Scholar 

  2. Barowy, D.W., Gochev, D., Berger, E.D.: CheckCell: data debugging for spread-sheets. In: OOPSLA, pp. 507–523 (2014)

    Google Scholar 

  3. Bastani, O., Zhang, X., Solar-Lezama, A.: Verifying Fairness Properties via Concentration. CoRR, abs/1812.02573 (2018)

    Google Scholar 

  4. Chaudhuri, S., Gulwani, S., Lublinerman, R.: Continuity and robustness of programs. Commun. ACM 55(8), 107–115 (2012)

    CrossRef  Google Scholar 

  5. Cheney, J., Ahmed, A., Acar, U.A.: Provenance as dependency analysis. Math. Struct. Comput. Sci. 21(6), 1301–1337 (2011)

    MathSciNet  CrossRef  Google Scholar 

  6. Cheng, T., Rival, X.: An abstract domain to infer types over zones in spreadsheets. In: Miné, A., Schmidt, D. (eds.) SAS 2012. LNCS, vol. 7460, pp. 94–110. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33125-1_9

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  8. Cousot, P., Cousot, R.: Static determination of dynamic properties of programs. In: Second International Symposium on Programming, pp. 106–130 (1976)

    Google Scholar 

  9. Cousot, P., Monerau, M.: Probabilistic abstract interpretation. In: ESOP, pp. 169–193 (2012)

    CrossRef  Google Scholar 

  10. Datta, A., Fredrikson, M., Ko, G., Mardziel, P., Sen, S.: Use privacy in data-driven systems: theory and experiments with machine learnt programs. In: CCS, pp. 1193–1210 (2017)

    Google Scholar 

  11. Feret, J.: Static analysis of digital filters. In: ESOP, pp. 33–48 (2004)

    CrossRef  Google Scholar 

  12. Filieri, A., Pasareanu, C.S., Visser, W.: Reliability analysis in symbolic pathfinder. In: ICSE, pp. 622–631 (2013)

    Google Scholar 

  13. Galhotra, S., Brun, Y., Meliou, A.: Fairness testing: testing software for discrimination. In: FSE, pp. 498–510 (2017)

    Google Scholar 

  14. Gehr, T., et al.: AI2: safety and robustness certification of neural networks with abstract interpretation. In: S & P, pp. 3–18 (2018)

    Google Scholar 

  15. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. CoRR, abs/1806.00069 (2018)

    Google Scholar 

  16. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)

    Google Scholar 

  17. Goubault, E., Putot, S.: Robustness analysis of finite precision implementations. In: APLAS, pp. 50–57 (2013)

    Google Scholar 

  18. Hennessy, M., Power, J.F.: An analysis of rule coverage as a criterion in generating minimal test suites for grammar-based software. In: ASE, pp. 104–113 (2005)

    Google Scholar 

  19. Herndon, T., Ash, M., Pollin, R.: Does high public debt consistently stifle economic growth? a critique of reinhart and rogoff. Cambridge J. Econ. 38(2), 257–279 (2014)

    CrossRef  Google Scholar 

  20. Huang, R., Xu, B., Schuurmans, D., Szepesvári, C.: Learning with a Strong Adversary. CoRR, abs/1511.03034 (2015)

    Google Scholar 

  21. Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: CAV, pp. 3–29 (2017)

    Google Scholar 

  22. Julian, K.D., Lopez, J., Brush, J.S., Owen, M.P., Kochenderfer, M.J.: Policy compression for aircraft collision avoidance systems. In: DASC, pp. 1–10 (2016)

    Google Scholar 

  23. Katz, G., Barrett, C.W., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: CAV, pp. 97–117 (2017)

    CrossRef  Google Scholar 

  24. Kusner, M., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: NIPS, pp. 4069–4079 (2017)

    Google Scholar 

  25. Majumdar, R., Saha, I.: Symbolic robustness analysis. In: RTSS, pp. 355–363 (2009)

    Google Scholar 

  26. Mallat, S.: Understanding deep convolutional networks. Phil. Trans. Royal Soc. A: Math., Phys. Eng. Sci. 374, 20150203 (2016)

    CrossRef  Google Scholar 

  27. Mencinger, J., Aristovnik, A., Verbič, M.: The impact of growing public debt on economic growth in the european union. Amfiteatru Econ. 16(35), 403–414 (2014)

    Google Scholar 

  28. Miné, A.: Symbolic methods to enhance the precision of numerical abstract domains. In: VMCAI, pp. 348–363 (2006)

    Google Scholar 

  29. Miné, A.: The octagon abstract domain. Higher-Order Symb. Comput. 19(1), 31–100 (2006)

    CrossRef  Google Scholar 

  30. Mirman, M., Gehr, T., Vechev, M.T.: Differentiable abstract interpretation for provably robust neural networks. In: ICML, pp. 3575–3583 (2018)

    Google Scholar 

  31. Nguyen, A.M., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: CVPR, pp. 427–436 (2015)

    Google Scholar 

  32. Pei, K., Cao, Y., Yang, J., Jana, S.: DeepXplore: automated whitebox testing of deep learning systems. In: SOSP, pp. 1–18 (2017)

    Google Scholar 

  33. Pulina, L., Tacchella, A.: An abstraction-refinement approach to verification of artificial neural networks. In: CAV, pp. 243–257 (2010)

    Google Scholar 

  34. Rival, X., Mauborgne, L.: The trace partitioning abstract domain. Trans. Program. Lang. Syst. 29(5), 26 (2007)

    CrossRef  Google Scholar 

  35. Rothermel, G., Burnett, M.M., Li, L., DuPuis, C., Sheretov, A.: A methodology for testing spreadsheets. Trans. Softw. Eng. Methodol. 10(1), 110–147 (2001)

    CrossRef  Google Scholar 

  36. Sankaranarayanan, S., Chakarov, A., Gulwani, S.: Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In: PLDI, pp. 447–458 (2013)

    Google Scholar 

  37. Singh, G., Gehr, T., Püschel, M., Vechev, M.T.: An abstract domain for certifying neural networks. In: PACMPL, vol. 3(POPL), pp. 41:1–41:30 (2019)

    CrossRef  Google Scholar 

  38. Smith, G.: Principles of secure information flow analysis. Malware Detection, vol. 27. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-44599-1_13

    CrossRef  Google Scholar 

  39. Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR (2014)

    Google Scholar 

  40. Tabacof, P., Valle, E.: Exploring the space of adversarial images. In: IJCNN, pp. 426–433 (2016)

    Google Scholar 

  41. Urban, C., Müller, P.: An abstract interpretation framework for input data usage. In: ESOP, pp. 683–710 (2018)

    CrossRef  Google Scholar 

  42. Weiser, M.: Program slicing. Trans. Softw. Eng. 10(4), 352–357 (1984)

    CrossRef  Google Scholar 

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Urban, C. (2019). Static Analysis of Data Science Software. In: Chang, BY. (eds) Static Analysis. SAS 2019. Lecture Notes in Computer Science(), vol 11822. Springer, Cham. https://doi.org/10.1007/978-3-030-32304-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-32304-2_2

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