research-article

Breadcrumbs: efficient context sensitivity for dynamic bug detection analyses

Authors:

Michael D. Bond,

Graham Z. Baker,

Samuel Z. GuyerAuthors Info & Claims

PLDI '10: Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 13 - 24

https://doi.org/10.1145/1806596.1806599

Published: 05 June 2010 Publication History

Abstract

Calling context--the set of active methods on the stack--is critical for understanding the dynamic behavior of large programs. Dynamic program analysis tools, however, are almost exclusively context insensitive because of the prohibitive cost of representing calling contexts at run time. Deployable dynamic analyses, in particular, have been limited to reporting only static program locations.

This paper presents Breadcrumbs, an efficient technique for recording and reporting dynamic calling contexts. It builds on an existing technique for computing a compact (one word) encoding of each calling context that client analyses can use in place of a program location. The key feature of our system is a search algorithm that can reconstruct a calling context from its encoding using only a static call graph and a small amount of dynamic information collected at cold (infrequently executed) callsites. Breadcrumbs requires no offline training or program modifications, and handles all language features, including dynamic class loading.

We use Breadcrumbs to add context sensitivity to two dynamic analyses: a data-race detector and an analysis for diagnosing null pointer exceptions. On average, it adds 10% to 20% runtime overhead, depending on a tunable parameter that controls how much dynamic information is collected. Collecting less information lowers the overhead, but can result in a search space explosion. In some cases this causes reconstruction to fail, but in most cases Breadcrumbs >produces non-trivial calling contexts that have the potential to significantly improve both the precision of the analyses and the quality of the bug reports.

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Beischl AKersten TBandle MGiceva JNeumann TBarbalace ABhatotia PAlvisi LCadar C(2021)Profiling dataflow systems on multiple abstraction levelsProceedings of the Sixteenth European Conference on Computer Systems10.1145/3447786.3456254(474-489)Online publication date: 21-Apr-2021
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Breadcrumbs: efficient context sensitivity for dynamic bug detection analyses

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Published In

cover image ACM Conferences

PLDI '10: Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2010

514 pages

ISBN:9781450300193

DOI:10.1145/1806596

General Chair:
Ben Zorn
Microsoft Research
,
Program Chair:
Alex Aiken
Stanford University

ACM SIGPLAN Notices Volume 45, Issue 6
PLDI '10
June 2010
496 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1809028
Issue’s Table of Contents

Copyright © 2010 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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Published: 05 June 2010

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PLDI '10: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 5 - 10, 2010

Ontario, Toronto, Canada

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Beischl AKersten TBandle MGiceva JNeumann TBarbalace ABhatotia PAlvisi LCadar C(2021)Profiling dataflow systems on multiple abstraction levelsProceedings of the Sixteenth European Conference on Computer Systems10.1145/3447786.3456254(474-489)Online publication date: 21-Apr-2021
https://dl.acm.org/doi/10.1145/3447786.3456254
Zhou TJantz MKulkarni PDoshi KSarkar VAmaral JKulkarni M(2019)Valence: variable length calling context encodingProceedings of the 28th International Conference on Compiler Construction10.1145/3302516.3307351(147-158)Online publication date: 16-Feb-2019
https://dl.acm.org/doi/10.1145/3302516.3307351
Cai CZhang QZuo ZNguyen KXu GSu Z(2018)Calling-to-reference context translation via constraint-guided CFL-reachabilityACM SIGPLAN Notices10.1145/3296979.319237853:4(196-210)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192378
Cai CZhang QZuo ZNguyen KXu GSu ZFoster JGrossman D(2018)Calling-to-reference context translation via constraint-guided CFL-reachabilityProceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3192366.3192378(196-210)Online publication date: 11-Jun-2018
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Wu RXiao XCheung SZhang HZhang C(2016)Casper: an efficient approach to call trace collectionACM SIGPLAN Notices10.1145/2914770.283761951:1(678-690)Online publication date: 11-Jan-2016
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Yadav PSingh P(2016)Comparative performance analysis of calling context profiling data structuresInternational Journal of System Assurance Engineering and Management10.1007/s13198-016-0447-x8:1(135-150)Online publication date: 30-Mar-2016
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D'Elia DDemetrescu CFinocchi I(2016)Mining hot calling contexts in small spaceSoftware—Practice & Experience10.1002/spe.234846:8(1131-1152)Online publication date: 1-Aug-2016
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Kim CRhee JZhang HArora NJiang GZhang XXu D(2014)IntroPerfACM SIGMETRICS Performance Evaluation Review10.1145/2637364.259200842:1(235-247)Online publication date: 16-Jun-2014
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