Analyzing Data Structure Growth Over Time to Facilitate Memory Leak Detection

Weninger, Markus; Gander, Elias; Mössenböck, Hanspeter

doi:10.1145/3297663.3310297

Cited by 10 publications

(10 citation statements)

References 29 publications

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“…Memory leaks [35] in managed languages occur if objects no longer needed remain reachable from garbage collection roots (e.g., static fields or local variables) due to programming errors. For example, objects may accumulate over time when a developer forgets to remove them from long-living data structures [115]. Such leaks lead to a growing memory footprint, which at some point will cause an application to crash.…”

Section: Memory Leaksmentioning

confidence: 99%

“…A user following the top-down approach first selects a GC root or a heap object that keeps alive many other objects. The user then inspects the objects reachable from this root or object and searches for possible cut points in the path [114,115]. Metric-based techniques derive metrics from the heap state that allow to analyze the heap state by revealing fields, objects, classes or packages that are likely involved in memory anomalies [19,20,77].…”

Section: Single Point In Timementioning

confidence: 99%

“…Extensively allocated objects can then be considered for reuse, caching, or removal. Other approaches allow to automatically detect growing data structures [115,117], or to visualize the evolution of the memory composition over time [120,121].…”

Section: Evolution Over Timementioning

confidence: 99%

“…The AntTracks Analyzer then parses trace files by incrementally processing these events, thereby allowing to reconstruct the heap state for every GC point [7]. Various memory analyses can be performed with AntTracks, including heap state analysis [114,119,122], data structure growth analysis [115,117], and heap evolution visualization [120].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study

Weninger

Grünbacher

Gander

et al. 2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

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Memory analysis tools are essential for finding and fixing anomalies in the memory usage of software systems (e.g., memory leaks). Although numerous tools are available, hardly any empirical studies exist on their usefulness for developers in typical usage scenarios. Instead, most evaluations are limited to reporting performance metrics. We thus conducted a study to empirically assess the usefulness of the interactive memory analysis tool AntTracks Analyzer. Specifically, we first report findings from assessing the tool using a cognitive walkthrough, guided by the Cognitive Dimensions of Notations Framework. We then present the results of a qualitative user study involving 14 subjects who used AntTracks to detect and resolve memory anomalies. We report lessons learned from the study and implications for developers of interactive memory analysis tools. We hope that our results will help researchers and developers of memory analysis tools in defining, selecting, and improving tool capabilities.

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Section: Memory Leaksmentioning

confidence: 99%

Section: Single Point In Timementioning

confidence: 99%

Section: Evolution Over Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study

Weninger

Grünbacher

Gander

et al. 2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

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show abstract

“…Despite great efforts having already been devoted to the root cause diagnosis of memory bloat in a programme execution with bloat phenomena observed, for example, Ref. [2,[4][5][6][7][8][9], challenges still remain regarding how to first find those memory bloated programme executions.…”

Section: Introductionmentioning

confidence: 99%

Prioritising test scripts for the testing of memory bloat in web applications

Qian

Zhou

2022

IET Software

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Memory bloat frequently occurs in web applications. It affects system performance and may even cause out-of-memory crashes. For web applications, testers often do performance testing that repeatedly runs test scripts to reveal potential memory bloats. Under that kind of testing, without guidance to determine the running order of test scripts, time may be wasted on testing with those non-bloat-inducing scripts. To address the problem, a test script prioritisation approach is proposed for the testing of memory bloat in Java web applications. The approach predicts which test scripts are more likely to make the underlying web application exhibit memory bloat phenomena by using a learning-to-rank technique. With this prediction, the execution of test scripts can be prioritised, and the revealing of memory bloat can thereby be accelerated. The experiments on a group of web applications obtained from Github and SourceForge show that the proposed prioritisation approach is effective.

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