Abstract
We propose to harness Angluin’s \(L^*\) algorithm for learning a deterministic finite automaton that describes the possible scenarios under which a given program error occurs. The alphabet of this automaton is given by the user (for instance, a subset of the function call sites or branches), and hence the automaton describes a user-defined abstraction of those scenarios. More generally, the same technique can be used for visualising the behavior of a program or parts thereof. This can be used, for example, for visually comparing different versions of a program, by presenting an automaton for the behavior in the symmetric difference between them, or for assisting in merging several development branches. We present initial experiments that demonstrate the power of an abstract visual representation of errors and of program segments.
This work was supported in part by the Google Faculty Research Award 2014.
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Notes
- 1.
Source code for all the programs mentioned in this article is available online from [1].
- 2.
This is a simplified upper bound of the complexity of the \(L^*\) algorithm.
- 3.
References
Angluin, D.: Learning \(k\)-bounded context-free grammars. Technical report, Dept. of Computer Science, Yale University (1987)
Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)
Angluin, D., Fisman, D.: Learning regular omega languages. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds.) ALT 2014. LNCS, vol. 8776, pp. 125–139. Springer, Heidelberg (2014)
Angluin, D., Kharitonov, M.: When won’t membership queries help? (extended abstract). In: Proceedings of 23rd STOC, pp. 444–454. ACM (1991)
Ball, T., Naik, M., Rajamani, S.K.: From symptom to cause: localizing errors in counterexample traces. In: Proceedings of 30th POPL, pp. 97–105 (2003)
Basu, S., Saha, D., Lin, Y.-J., Smolka, S.A.: Generation of all counter-examples. In: König, H., Heiner, M., Wolisz, A. (eds.) FORTE 2003. LNCS, vol. 2767, pp. 79–94. Springer, Heidelberg (2003)
Beer, I., Ben-David, S., Chockler, H., Orni, A., Trefler, R.J.: Explaining counterexamples using causality. Formal Methods Syst. Des. 40(1), 20–40 (2012)
Beyer, D.: Software verification and verifiable witnesses. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 401–416. Springer, Heidelberg (2015)
Bollig, B., Katoen, J.-P., Kern, C., Leucker, M., Neider, D., Piegdon, D.R.: libalf: the automata learning framework. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 360–364. Springer, Heidelberg (2010)
Botinc̆an, M., Babić, D.: Sigma*: symbolic learning of input-output specifications. In: Proc. of 40th POPL, pp. 443–456. ACM (2013)
Clarke, E., Kroning, D., Lerda, F.: A tool for checking ANSI-C programs. In: Jensen, K., Podelski, A. (eds.) TACAS 2004. LNCS, vol. 2988, pp. 168–176. Springer, Heidelberg (2004)
Copty, F., Irron, A., Weissberg, O., Kropp, N.P., Kamhi, G.: Efficient debugging in a formal verification environment. STTT 4(3), 335–348 (2003)
Farzan, A., Chen, Y.-F., Clarke, E.M., Tsay, Y.-K., Wang, B.-Y.: Extending automated compositional verification to the full class of omega-regular languages. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 2–17. Springer, Heidelberg (2008)
Giannakopoulou, D., Rakamarić, Z., Raman, V.: Symbolic learning of component interfaces. In: Miné, A., Schmidt, D. (eds.) SAS 2012. LNCS, vol. 7460, pp. 248–264. Springer, Heidelberg (2012)
Groce, A., Chaki, S., Kroening, D., Strichman, O.: Error explanation with distance metrics. STTT 8(3), 229–247 (2006)
Hoenicke, J., Leino, K., Podelski, A., Schäf, M., Wies, T.: Doomed program points. Formal Methods Syst. Des. 37(2–3), 171–199 (2010)
Hopcroft, J., Motwani, R., Ullman, J.: Introduction to Automata Theory, Languages, and Computation, 2nd edn. Addison-Wesley, Reading (2000)
Leucker, M., Neider, D.: Learning minimal deterministic automata from inexperienced teachers. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part I. LNCS, vol. 7609, pp. 524–538. Springer, Heidelberg (2012)
The problem of automatic code merging (2012). http://www.personal.psu.edu/txl20/blogs/tks_tech_notes/2012/03/the-problem-of-automatic-code-merging.html
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Chapman, M., Chockler, H., Kesseli, P., Kroening, D., Strichman, O., Tautschnig, M. (2015). Learning the Language of Error. In: Finkbeiner, B., Pu, G., Zhang, L. (eds) Automated Technology for Verification and Analysis. ATVA 2015. Lecture Notes in Computer Science(), vol 9364. Springer, Cham. https://doi.org/10.1007/978-3-319-24953-7_9
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