research-article

Consistency analysis of decision-making programs

Authors:

Swarat Chaudhuri,

Zachary KincaidAuthors Info & Claims

POPL '14: Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

Pages 555 - 567

https://doi.org/10.1145/2535838.2535858

Published: 08 January 2014 Publication History

Abstract

Applications in many areas of computing make discrete decisions under uncertainty, for reasons such as limited numerical precision in calculations and errors in sensor-derived inputs. As a result, individual decisions made by such programs may be nondeterministic, and lead to contradictory decisions at different points of an execution. This means that an otherwise correct program may execute along paths, that it would not follow under its ideal semantics, violating essential program invariants on the way. A program is said to be consistent if it does not suffer from this problem despite uncertainty in decisions.

In this paper, we present a sound, automatic program analysis for verifying that a program is consistent in this sense. Our analysis proves that each decision made along a program execution is consistent with the decisions made earlier in the execution. The proof is done by generating an invariant that abstracts the set of all decisions made along executions that end at a program location l, then verifying, using a fixpoint constraint-solver, that no contradiction can be derived when these decisions are combined with new decisions made at l.

We evaluate our analysis on a collection of programs implementing algorithms in computational geometry. Consistency is known to be a critical, frequently-violated, and thoroughly studied correctness property in geometry, but ours is the first attempt at automated verification of consistency of geometric algorithms. Our benchmark suite consists of implementations of convex hull computation, triangulation, and point location algorithms. On almost all examples that are not consistent (with two exceptions), our analysis is able to verify consistency within a few minutes.

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https://dl.acm.org/doi/10.1145/3093333.3009846
Chiang WBaranowski MBriggs ISolovyev AGopalakrishnan GRakamarić ZCastagna GGordon A(2017)Rigorous floating-point mixed-precision tuningProceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages10.1145/3009837.3009846(300-315)Online publication date: 1-Jan-2017
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Index Terms

Consistency analysis of decision-making programs
1. Software and its engineering
  1. Software organization and properties
    1. Software functional properties
      1. Correctness
2. Theory of computation
  1. Logic
    1. Proof theory
  2. Semantics and reasoning
    1. Program reasoning
      1. Program analysis
    2. Program semantics

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cover image ACM Conferences

POPL '14: Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

January 2014

702 pages

ISBN:9781450325448

DOI:10.1145/2535838

General Chair:
Suresh Jagannathan
Purdue University, USA
,
Program Chair:
Peter Sewell
University of Cambridge, UK

ACM SIGPLAN Notices Volume 49, Issue 1
POPL '14
January 2014
661 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2578855
Editor:
Mark W. Bailey
Hamilton College, Clinton, NY
Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 08 January 2014

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POPL '14: The 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

January 22 - 24, 2014

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Overall Acceptance Rate 824 of 4,130 submissions, 20%

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Cited By

Zhang XKong WJiang JHou GFukuda A(2019)Steering Interpolants Generation with Efficient Interpolation Abstraction Exploration2019 International Symposium on Theoretical Aspects of Software Engineering (TASE)10.1109/TASE.2019.00-11(113-120)Online publication date: Jul-2019
https://doi.org/10.1109/TASE.2019.00-11
Chiang WBaranowski MBriggs ISolovyev AGopalakrishnan GRakamarić Z(2017)Rigorous floating-point mixed-precision tuningACM SIGPLAN Notices10.1145/3093333.300984652:1(300-315)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1145/3093333.3009846
Chiang WBaranowski MBriggs ISolovyev AGopalakrishnan GRakamarić ZCastagna GGordon A(2017)Rigorous floating-point mixed-precision tuningProceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages10.1145/3009837.3009846(300-315)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1145/3009837.3009846
Leroux JRümmer PSubotić P(2016)Guiding Craig interpolation with domain-specific abstractionsActa Informatica10.1007/s00236-015-0236-z53:4(387-424)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s00236-015-0236-z
Chiang WGopalakrishnan GRakamarić Z(2015)Practical Floating-Point Divergence DetectionRevised Selected Papers of the 28th International Workshop on Languages and Compilers for Parallel Computing - Volume 951910.1007/978-3-319-29778-1_17(271-286)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-29778-1_17

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