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Thin slicing

Authors:

Manu Sridharan,

Stephen J. Fink,

Rastislav BodikAuthors Info & Claims

ACM SIGPLAN Notices, Volume 42, Issue 6

Pages 112 - 122

https://doi.org/10.1145/1273442.1250748

Published: 10 June 2007 Publication History

Abstract

Program slicing systematically identifies parts of a program relevant to a seed statement. Unfortunately, slices of modern programs often grow too large for human consumption. We argue that unwieldy slices arise primarily from an overly broad definition of relevance, rather than from analysis imprecision. While a traditional slice includes all statements that may affect a point of interest, not all such statements appear equally relevant to a human.

As an improved method of finding relevant statements, we propose thin slicing. A thin slice consists only of producer statements for the seed, i.e., those statements that help compute and copy avalue to the seed. Statements that explain why producers affect the seed are excluded. For example, for a seed that reads a value from a container object, a thin slice includes statements that store the value into the container, but excludes statements that manipulate pointers to the container itself. Thin slices can also be hierarchically expanded to include statements explaining how producers affect the seed, yielding a traditional slice in the limit.

We evaluated thin slicing for a set of debugging and program understanding tasks. The evaluation showed that thin slices usually included the desired statements for the tasks (e.g., the buggy statement for a debugging task). Furthermore, in simulated use of a slicing tool, thin slices revealed desired statements after inspecting 3.3 times fewer statements than traditional slicing for our debugging tasks and 9.4 times fewer statements for our program understanding tasks. Finally, our thin slicing algorithm scales well to relatively large Java benchmarks, suggesting that thin slicing represents an attractive option for practical tools.

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Cited By

Galindo CPérez SSilva J(2025)The expression dependence graphJournal of Logical and Algebraic Methods in Programming10.1016/j.jlamp.2024.101016142(101016)Online publication date: Jan-2025
https://doi.org/10.1016/j.jlamp.2024.101016
Galindo CLlorens MPérez SSilva J(2023)Slicing Shared-Memory Concurrent Programs The Threaded System Dependence Graph Revisited2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58846.2023.00019(73-83)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICSME58846.2023.00019
Mertz JNunes I(2023)Software runtime monitoring with adaptive sampling rate to collect representative samples of execution tracesJournal of Systems and Software10.1016/j.jss.2023.111708202:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111708
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Index Terms

Thin slicing
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Information & Contributors

Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 42, Issue 6

Proceedings of the 2007 PLDI conference

June 2007

491 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/1273442

Issue’s Table of Contents

PLDI '07: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation
June 2007
508 pages
ISBN:9781595936332
DOI:10.1145/1250734
General Chair:
Jeanne Ferrante
University of California, San Diego, USA
,
Program Chair:
Kathryn S. McKinley
University of Texas at Austin, USA

Copyright © 2007 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2007

Published in SIGPLAN Volume 42, Issue 6

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Cited By

Galindo CPérez SSilva J(2025)The expression dependence graphJournal of Logical and Algebraic Methods in Programming10.1016/j.jlamp.2024.101016142(101016)Online publication date: Jan-2025
https://doi.org/10.1016/j.jlamp.2024.101016
Galindo CLlorens MPérez SSilva J(2023)Slicing Shared-Memory Concurrent Programs The Threaded System Dependence Graph Revisited2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58846.2023.00019(73-83)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICSME58846.2023.00019
Mertz JNunes I(2023)Software runtime monitoring with adaptive sampling rate to collect representative samples of execution tracesJournal of Systems and Software10.1016/j.jss.2023.111708202:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111708
Dutta AMitra PMall R(2022)An Investigation into Effective Fault LocalizationIntelligent Technologies: Concepts, Applications, and Future Directions10.1007/978-981-19-1021-0_4(83-112)Online publication date: 22-May-2022
https://doi.org/10.1007/978-981-19-1021-0_4
Galindo CKrinke JPérez SSilva J(2022)Field-Sensitive Program SlicingSoftware Engineering and Formal Methods10.1007/978-3-031-17108-6_5(74-90)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17108-6_5
Tan TLi YMa XXu CSmaragdakis Y(2021)Making pointer analysis more precise by unleashing the power of selective context sensitivityProceedings of the ACM on Programming Languages10.1145/34855245:OOPSLA(1-27)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485524
Hough KBell J(2021)A Practical Approach for Dynamic Taint Tracking with Control-flow RelationshipsACM Transactions on Software Engineering and Methodology10.1145/348546431:2(1-43)Online publication date: 24-Dec-2021
https://dl.acm.org/doi/10.1145/3485464
Wang HLin YYang ZSun JLiu YDong JZheng QLiu T(2021)Explaining Regressions via Alignment Slicing and MendingIEEE Transactions on Software Engineering10.1109/TSE.2019.294956847:11(2421-2437)Online publication date: 1-Nov-2021
https://doi.org/10.1109/TSE.2019.2949568
Dutta ASrivastava SGodboley SMohapatra D(2021)Combi-FL: Neural network and SBFL based fault localization using mutation analysisJournal of Computer Languages10.1016/j.cola.2021.10106466(101064)Online publication date: Oct-2021
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Dutta AGodboley S(2021)MSFL: A Model for Fault Localization Using Mutation-Spectra TechniqueLean and Agile Software Development10.1007/978-3-030-67084-9_10(156-173)Online publication date: 6-Jan-2021
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