research-article

Analyzing Data Structure Growth Over Time to Facilitate Memory Leak Detection

Authors:

Markus Weninger,

Hanspeter MössenböckAuthors Info & Claims

ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

Pages 273 - 284

https://doi.org/10.1145/3297663.3310297

Published: 04 April 2019 Publication History

Abstract

Memory leaks are a major threat in modern software systems. They occur if objects are unintentionally kept alive longer than necessary and are often indicated by continuously growing data structures.

While there are various state-of-the-art memory monitoring tools, most of them share two critical shortcomings: (1) They have no knowledge about the monitored application's data structures and (2) they support no or only rudimentary analysis of the application's data structures over time.

This paper encompasses novel techniques to tackle both of these drawbacks. It presents a domain-specific language (DSL) that allows users to describe arbitrary data structures, as well as an algorithm to detect instances of these data structures in reconstructed heaps. In addition, we propose techniques and metrics to analyze and measure the evolution of data structure instances over time. This allows us to identify those instances that are most likely involved in a memory leak. These concepts have been integrated into AntTracks, a trace-based memory monitoring tool. We present our approach to detect memory leaks in several real-world applications, showing its applicability and feasibility.

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Cited By

Remil YBendimerad AChambard MMathonat RPlantevit MKaytoue M(2023)Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00159(1221-1230)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00159
Blanco ABergel AAlcocer JCordova A(2022)Visualizing Memory Consumption with Vismep2022 Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT55257.2022.00019(108-118)Online publication date: Oct-2022
https://doi.org/10.1109/VISSOFT55257.2022.00019
Weninger MGander EMössenböck H(2021)Guided ExplorationProceedings of the ACM on Human-Computer Interaction10.1145/34617315:EICS(1-34)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3461731
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cover image ACM Conferences

ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

April 2019

348 pages

ISBN:9781450362399

DOI:10.1145/3297663

General Chairs:
Varsha Apte
IIT Bombay, India
,
Antinisca Di Marco
University of L'Aquila, Italy
,
Program Chairs:
Marin Litoiu
York University, Canada
,
José Merseguer
Universidad de Zaragoza, Spain

Copyright © 2019 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 the author(s) 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: 04 April 2019

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Christian Doppler Forschungsgesellschaft

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ICPE '19

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ICPE '19: Tenth ACM/SPEC International Conference on Performance Engineering

April 7 - 11, 2019

Mumbai, India

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ICPE '19 Paper Acceptance Rate 13 of 71 submissions, 18%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Remil YBendimerad AChambard MMathonat RPlantevit MKaytoue M(2023)Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00159(1221-1230)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00159
Blanco ABergel AAlcocer JCordova A(2022)Visualizing Memory Consumption with Vismep2022 Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT55257.2022.00019(108-118)Online publication date: Oct-2022
https://doi.org/10.1109/VISSOFT55257.2022.00019
Weninger MGander EMössenböck H(2021)Guided ExplorationProceedings of the ACM on Human-Computer Interaction10.1145/34617315:EICS(1-34)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3461731
Weninger MGrünbacher PGander ESchörgenhumer A(2020)Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User StudyProceedings of the ACM on Human-Computer Interaction10.1145/33949774:EICS(1-37)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1145/3394977
Weninger MMakor LMossenbock H(2020)Memory Cities: Visualizing Heap Memory Evolution Using the Software City Metaphor2020 Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT51673.2020.00017(110-121)Online publication date: Sep-2020
https://doi.org/10.1109/VISSOFT51673.2020.00017
Weninger MGander EMössenböck HHosking AFinocchi I(2019)Detection of suspicious time windows in memory monitoringProceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes10.1145/3357390.3361025(95-104)Online publication date: 21-Oct-2019
https://dl.acm.org/doi/10.1145/3357390.3361025
Weninger MMakor LGander EMössenböck HApte VDi Marco ALitoiu MMerseguer J(2019)AntTracks TrendVizCompanion of the 2019 ACM/SPEC International Conference on Performance Engineering10.1145/3302541.3313100(29-32)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3302541.3313100

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