LeakSpot: detection and diagnosis of memory leaks in JavaScript applications

Rudafshani, Masoomeh; Ward, Paul A. S.

doi:10.1002/spe.2406

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…Based on the staleness measure of allocated objects, Rudaf et al proposed "LeakSpot" for detecting memory leaks in web applications [13]. It locates JavaScript allocation and reference sites that produce and retain increasing numbers of objects over time and uses staleness as a heuristic to identify memory leaks.…”

Section: Related Workmentioning

confidence: 99%

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Jindal,

Staab,

Kulkarni

et al. 2021

Preprint

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A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details.This paper addresses this challenge of detection of memory leaks in cloud-based infrastructure without having any internal knowledge by introducing two novel machine learning based algorithms: Linear Backward Regression (LBR) and Precog and, their two variants: Linear Backward Regression with Change Points Detection (LBRCPD) and Precog with Maximum Filteration (PrecogMF). These algorithms only use one metric i.e the system's memory utilization on which the application is deployed for detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by Huawei and it was found that the proposed PrecogMF algorithm achieves the highest accuracy score of 85%. The same algorithm also achieves this by decreasing the overall compute time by 80% when compared to LBR's compute time.The paper also presents the different memory leak patterns found in the various memory leak applications and are further classified into different classes based on their visual representation.

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Section: Related Workmentioning

confidence: 99%

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Jindal,

Staab,

Kulkarni

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the staleness measure of allocated objects, Rudaf et al proposed "LeakSpot" for detecting memory leaks in web applications [9]. It locates JavaScript allocation and reference sites that produce and retain increasing numbers of objects over time and uses staleness as a heuristic to identify memory leaks.…”

Section: Related Workmentioning

confidence: 99%

Online Memory Leak Detection in the Cloud-based Infrastructures

Jindal,

Staab,

Cardoso

et al. 2021

Preprint

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A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system's memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85% with less than half a second prediction time per virtual machine.

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“…Web application memory leak detectors: BL automatically debugs memory leaks in modern web applications; past work in this space is ineffective, out of date, or not sufficiently general. LeakSpot locates JavaScript allocation and reference sites that produce and retain increasing numbers of objects over time, and uses staleness as a heuristic to refine its output [52]. On real web applications, LeakSpot typically reports over 50 different allocation and reference sites that developers must manually inspect to identify and diagnose memory leaks.…”

Section: Related Workmentioning

confidence: 99%

“…The reason is that existing memory leak detection techniques are ineffective in the browser: leaks in web applications are fundamentally different from leaks in traditional C, C++, and Java programs. Staleness-based techniques assume leaked memory is rarely touched [8,25,48,52,74], but web applications regularly interact with leaked state (e.g., via event listeners). Growthbased techniques assume that leaked objects are uniquely owned or that leaked objects form strongly connected components in the heap graph [41,74].…”

Section: Introductionmentioning

confidence: 99%

BLeak: automatically debugging memory leaks in web applications

Vilk

Berger

2018

SIGPLAN Not.

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Despite the presence of garbage collection in managed languages like JavaScript, memory leaks remain a serious problem. In the context of web applications, these leaks are especially pervasive and difficult to debug. Web application memory leaks can take many forms, including failing to dispose of unneeded event listeners, repeatedly injecting iframes and CSS files, and failing to call cleanup routines in third-party libraries. Leaks degrade responsiveness by increasing GC frequency and overhead, and can even lead to browser tab crashes by exhausting available memory. Because previous leak detection approaches designed for conventional C, C++ or Java applications are ineffective in the browser environment, tracking down leaks currently requires intensive manual effort by web developers. This paper introduces BLeak (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications. BLeak's algorithms leverage the observation that in modern web applications, users often repeatedly return to the same (approximate) visual state (e.g., the inbox view in Gmail). Sustained growth between round trips is a strong indicator of a memory leak. To use BLeak, a developer writes a short script (17-73 LOC on our benchmarks) to drive a web application in round trips to the same visual state. BLeak then automatically generates a list of leaks found along with their root causes, ranked by return on investment. Guided by BLeak, we identify and fix over 50 memory leaks in popular libraries and apps including Airbnb, AngularJS, Google Analytics, Google Maps SDK, and jQuery. BLeak's median precision is 100%; fixing the leaks it identifies reduces heap growth by an average of 94%, saving from 0.5 MB to 8 MB per round trip. We believe BLeak's approach to be broadly applicable beyond web applications, including to GUI applications on desktop and mobile platforms.

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LeakSpot: detection and diagnosis of memory leaks in JavaScript applications

Cited by 11 publications

References 29 publications

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Online Memory Leak Detection in the Cloud-based Infrastructures

BLeak: automatically debugging memory leaks in web applications

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