research-article

Static analysis for probabilistic programs: inferring whole program properties from finitely many paths

Authors:

Sriram Sankaranarayanan,

Aleksandar Chakarov,

Sumit GulwaniAuthors Info & Claims

PLDI '13: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 447 - 458

https://doi.org/10.1145/2491956.2462179

Published: 16 June 2013 Publication History

Abstract

We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyber-physical systems. Correctness properties of such programs take the form of queries that seek the probabilities of assertions over program variables. We present a static analysis approach that provides guaranteed interval bounds on the values (assertion probabilities) of such queries. First, we observe that for probabilistic programs, it is possible to conclude facts about the behavior of the entire program by choosing a finite, adequate set of its paths. We provide strategies for choosing such a set of paths and verifying its adequacy. The queries are evaluated over each path by a combination of symbolic execution and probabilistic volume-bound computations. Each path yields interval bounds that can be summed up with a "coverage" bound to yield an interval that encloses the probability of assertion for the program as a whole. We demonstrate promising results on a suite of benchmarks from many different sources including robotic manipulators and medical decision making programs.

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Cited By

Bowyer SHeap TAitchison LKiyavash NMooij J(2024)Using autodiff to estimate posterior moments, marginals and samplesProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702695(394-417)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702695
Chatterjee KGoharshady ENovotný PŽikelić Đ(2024)Equivalence and Similarity Refutation for Probabilistic ProgramsProceedings of the ACM on Programming Languages10.1145/36564628:PLDI(2098-2122)Online publication date: 20-Jun-2024
https://doi.org/10.1145/3656462
Wang PYang TFu HLi GOng C(2024)Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial SolvingProceedings of the ACM on Programming Languages10.1145/36564328:PLDI(1361-1386)Online publication date: 20-Jun-2024
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Index Terms

Static analysis for probabilistic programs: inferring whole program properties from finitely many paths
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Formal software verification
  2. Software organization and properties
    1. Software functional properties
      1. Formal methods

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cover image ACM Conferences

PLDI '13: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2013

546 pages

ISBN:9781450320146

DOI:10.1145/2491956

General Chair:
Hans-J. Boehm
HP Labs
,
Program Chair:
Cormac Flanagan
University of California, Santa Cruz

ACM SIGPLAN Notices Volume 48, Issue 6
PLDI '13
June 2013
515 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2499370
Issue’s Table of Contents

Copyright © 2013 ACM.

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Published: 16 June 2013

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PLDI '13: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 16 - 19, 2013

Washington, Seattle, USA

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PLDI '13 Paper Acceptance Rate 46 of 267 submissions, 17%;

Overall Acceptance Rate 406 of 2,067 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.5555/3702676.3702695
Chatterjee KGoharshady ENovotný PŽikelić Đ(2024)Equivalence and Similarity Refutation for Probabilistic ProgramsProceedings of the ACM on Programming Languages10.1145/36564628:PLDI(2098-2122)Online publication date: 20-Jun-2024
https://doi.org/10.1145/3656462
Wang PYang TFu HLi GOng C(2024)Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial SolvingProceedings of the ACM on Programming Languages10.1145/36564328:PLDI(1361-1386)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656432
Wu HCui ADeng Y(2024)An Assertion-Based Logic for Local Reasoning about Probabilistic ProgramsDependable Software Engineering. Theories, Tools, and Applications10.1007/978-981-96-0602-3_2(25-45)Online publication date: 25-Nov-2024
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Boreale MCollodi L(2023)Guaranteed Inference for Probabilistic Programs: A Parallelisable, Small-Step Operational ApproachVerification, Model Checking, and Abstract Interpretation10.1007/978-3-031-50521-8_7(141-162)Online publication date: 30-Dec-2023
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Beutner ROng CZaiser FJhala RDillig I(2022)Guaranteed bounds for posterior inference in universal probabilistic programmingProceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3519939.3523721(536-551)Online publication date: 9-Jun-2022
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Gerrard MBorges MDwyer MFilieri A(2022)Conditional Quantitative Program AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2020.301677848:4(1212-1227)Online publication date: 1-Apr-2022
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