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Control-flow recovery from partial failure reports

Authors:

Alexander Brooks,

Loris D'Antoni,

Ben LiblitAuthors Info & Claims

ACM SIGPLAN Notices, Volume 52, Issue 6

Pages 390 - 405

https://doi.org/10.1145/3140587.3062368

Published: 14 June 2017 Publication History

Editorial Notes

A corrigendum was issued for this article on January 14, 2019. This can be found under the Source Materials tab.

Abstract

Debugging is difficult. When software fails in production, debugging is even harder, as failure reports usually provide only an incomplete picture of the failing execution. We present a system that answers control-flow queries posed by developers as formal languages, indicating whether the query expresses control flow that is possible or impossible for a given failure report. We consider three separate approaches that trade off precision, expressiveness for failure constraints, and scalability. We also introduce a new subclass of regular languages, the unreliable trace languages, which are particularly suited to answering control-flow queries in polynomial time. Our system answers queries remarkably efficiently when we encode failure constraints and user queries entirely as unreliable trace languages.

Supplementary Material

PDF File (pldi17-main189-corrigendum.pdf)

Corrigendum to "Control-flow recovery from partial failure reports", by Ohmann et al., PLDI '17 Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Auxiliary Archive (pldi17-main189-s.zip)

The archive contains three articles of supplemental material: (1) a virtual machine artifact for reproducing the experimental results from the paper, (2) a report of supplemental proofs for the techniques and language classes introduced in the conference paper, and (3) the result data used to generate all plots in the paper.

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Cited By

Zhang HHao YLatif SBassily RRountev A(2020)Differentially-private software frequency profiling under linear constraintsProceedings of the ACM on Programming Languages10.1145/34282714:OOPSLA(1-24)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3428271
Liu XYang CLi DZhou YLi SChen JChen Z(2023)Adonis: Practical and Efficient Control Flow Recovery through OS-level TracesACM Transactions on Software Engineering and Methodology10.1145/360718733:1(1-27)Online publication date: 4-Jul-2023
https://dl.acm.org/doi/10.1145/3607187
Zuo ZJi KWang YTao WWang LLi XXu GFreund SYahav E(2021)JPortal: precise and efficient control-flow tracing for JVM programs with Intel processor traceProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454096(1080-1094)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454096
Show More Cited By

Index Terms

Control-flow recovery from partial failure reports
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance
2. Theory of computation
  1. Formal languages and automata theory
    1. Regular languages

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Information & Contributors

Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 52, Issue 6

PLDI '17

June 2017

708 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/3140587

Editor:
Matthew Fluet

Issue’s Table of Contents

PLDI 2017: Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation
June 2017
708 pages
ISBN:9781450349888
DOI:10.1145/3062341
General Chair:
Albert Cohen
Inria, France
,
Program Chair:
Martin Vechev
DeepCode, Switzerland / ETH Zurich, Switzerland

Copyright © 2017 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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Publication History

Published: 14 June 2017

Published in SIGPLAN Volume 52, Issue 6

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Cited By

Zhang HHao YLatif SBassily RRountev A(2020)Differentially-private software frequency profiling under linear constraintsProceedings of the ACM on Programming Languages10.1145/34282714:OOPSLA(1-24)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3428271
Liu XYang CLi DZhou YLi SChen JChen Z(2023)Adonis: Practical and Efficient Control Flow Recovery through OS-level TracesACM Transactions on Software Engineering and Methodology10.1145/360718733:1(1-27)Online publication date: 4-Jul-2023
https://dl.acm.org/doi/10.1145/3607187
Zuo ZJi KWang YTao WWang LLi XXu GFreund SYahav E(2021)JPortal: precise and efficient control-flow tracing for JVM programs with Intel processor traceProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454096(1080-1094)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454096
Heidari DDoostali S(2021)The application of hypergroups in symbolic executions and finite automataSoft Computing10.1007/s00500-021-05812-325:11(7247-7256)Online publication date: 22-Apr-2021
https://doi.org/10.1007/s00500-021-05812-3
Guo WMu DXing XDu MSong DHeninger NTraynor P(2019)DEEPVSAProceedings of the 28th USENIX Conference on Security Symposium10.5555/3361338.3361462(1787-1804)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.5555/3361338.3361462
D’Antoni LVeanes M(2017)The Power of Symbolic Automata and TransducersComputer Aided Verification10.1007/978-3-319-63387-9_3(47-67)Online publication date: 13-Jul-2017
https://doi.org/10.1007/978-3-319-63387-9_3

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