Abstract
We provide a prototype implementation of a recording-based debugger for imperative probabilistic programs supporting randomised choice, conditioning, and loops. In order to handle different branches of execution, we take a semantics-based approach that employs weakest preexpectations and that introduces iteration limits for approximating the behaviour of potentially non-terminating loops, while giving hard bounds on the corresponding value without loop iteration limits.
The work presented in this tool paper is based on the Master’s thesis by Alexander Hoppen [5].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The same logic also applies to prob- and while-statements. For loops, the user can jump into the loop’s body or exit the loop.
- 2.
Here, if-, prob and while-statements are viewed as single statements that include their bodies.
References
Aggarwal, S.K., Kumar, M.S.: Debuggers for programming languages. In: The Compiler Design Handbook, pp. 297–329. CRC Press (2002)
Corporation, M.: Mozilla RR debugger (2020). https://rr-project.org
Dijkstra, E.W.: A Discipline of Programming. Prentice-Hall, Hoboken (1976)
Gordon, A.D., Henzinger, T.A., Nori, A.V., Rajamani, S.K.: Probabilistic programming. In: Future of Software Engineering, pp. 167–181. ACM (2014)
Hoppen, A.: Design and implementation of an interactive exploration tool for probabilistic programs. Master’s thesis, RWTH Aachen University, Germany (2020). https://publications.rwth-aachen.de/record/802809
Hoppen, A., Noll, T.: A debugger for probabilistic programs. Technical report, RWTH Aachen University (2020). https://moves.rwth-aachen.de/wp-content/uploads/probabilistic-debugger-report.pdf
Katoen, J.-P., Gretz, F., Jansen, N., Kaminski, B.L., Olmedo, F.: Understanding probabilistic programs. In: Meyer, R., Platzer, A., Wehrheim, H. (eds.) Correct System Design. LNCS, vol. 9360, pp. 15–32. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23506-6_4
McIver, A., Morgan, C., Morgan, C.C.: Abstraction, Refinement and Proof for Probabilistic Systems. Springer, New York (2005). https://doi.org/10.1007/b138392
Nandi, C., Grossman, D., Sampson, A., Mytkowicz, T., McKinley, K.S.: Debugging probabilistic programs. In: MAPL 2017, pp. 18–26. ACM (2017)
Noll, T.: A debugger for probabilistic programs, October 2021. https://git.rwth-aachen.de/moves/probabilistic-debugger
Telles, M., Hsieh, Y.: The Science of Debugging. Coriolis (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hoppen, A., Noll, T. (2021). A Debugger for Probabilistic Programs. In: Calinescu, R., Păsăreanu, C.S. (eds) Software Engineering and Formal Methods. SEFM 2021. Lecture Notes in Computer Science(), vol 13085. Springer, Cham. https://doi.org/10.1007/978-3-030-92124-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-92124-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92123-1
Online ISBN: 978-3-030-92124-8
eBook Packages: Computer ScienceComputer Science (R0)