Memory leak detection in Java: Taxonomy and classification of approaches

Šor, Vladimir; Srirama, Satish Narayana

doi:10.1016/j.jss.2014.06.005

Cited by 17 publications

(6 citation statements)

References 2 publications

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“…Interactive memory analysis tools collect, process, transform, and visualize information about the memory footprint of software systems. Snapshot-based tools analyze a single point in time while trace-based tools allow users to explore a period of time [105]. For example, existing tools typically present the heap state of an application as a type histogram displaying the number of objects and bytes allocated for each type.…”

Section: Introductionmentioning

confidence: 99%

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.

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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: Introductionmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

show abstract

“…One insight for developing such general JavaScript memory leak detection techniques is to extend the existing techniques for Java-like languages [7] to JavaScript. Java suffers from memory leak problems similar to JavaScript's.…”

Section: Introductionmentioning

confidence: 99%

“…Another kind of technique detects memory leaks according to the structural information within the heap, such as the ownership relation [12], the data structure similarity and reoccurrence [13], etc. Besides, some other approaches also find leaks using the object lifetime information, such as the age of objects [7] and the staleness of objects (how long the object have not be used) [14].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Approach to Detect Memory Leaks in JavaScript (S)

Ju¹,

Wang²,

Zhou³

2018

International Conferences on Software Engineering and Knowledge Engineering

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Although with garbage collection support, many JavaScript programs still suffer from memory leaks. These leaks can affect application performance and even cause crashes, especially for single page websites. The existing work on JavaScript memory leak mainly focuses on the static detection of leaks toward certain leak patterns. The application scope of such approaches are limited. Previous techniques used for detecting memory leaks in Javalike languages might be extended to JavaScript. However, how to apply these techniques in JavaScript is still a problem. In this paper, we firstly present many common leak detection heuristics used in garbage-collected languages and investigate their effectiveness on JavaScript. According to the investigation results, we then propose a lightweight multi-snapshots based dynamic leak detection method for JavaScript. The initial experimental results show that the proposed approach is effective.

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“…According to Oracle, Inc. [1], over 3 billion mobile devices, 125 million televisions, and 97% of enterprise desktops are Java enabled, with more than 5 billion Java Cards currently in use. Java is a leading tool for embedded, web-based, and distributed software due to its rich feature set, which notably includes: its object-oriented paradigm; platform independence (portability); automatic garbage collection (decreasing code complexity and minimizing memory leaks); type safety; built-in networking and multithreading; extensive runtime, error checking, and application profiling to support development; and the Just-in-Time (JIT) compiler of the Java Virtual Machine (JVM), which approaches the speed of natively compiled code [2]- [15]. The developer community is continually expanding -with current estimates at 9 million -and an increasing number of universities offer Java in lieu of, for example, C/C++ [1], [12], [16]- [19].…”

Section: Introductionmentioning

confidence: 99%

UPC: Large-Scale Memory Efficient Java Primitive Collections

Hylock¹

2016

JSW

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Abstract:The Java programming language is versatile and robust, however, for large-scale applications with memory intensive processes, much is still to be desired. The public Java Development Kit (JDK) supports neither forcible object destruction nor large collection sizes (limited to 231-1) as found in choice computational languages such as Fortran and C/C++. Within Java's Hotspot, however, the non-public sun.misc.Unsafe class allows such features through basic off-heap functionality. The submitted UPC collections are fully integrated with Unsafe to provide large (i.e., 263-1), destructible arrays, lists, hash sets, hash maps, and matrices, consistent with advanced computational needs. Additionally, a customized version of OpenJDK 1.9 with bulk-operation Unsafe support and companion collections are provided. These tools are compared to Java and extent third-party primitive collections. Testing indicates UPC performs in a consistent to superior manner compared to the state-of-the-art, with greater improvements realized by way of the modified virtual machine.

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Memory leak detection in Java: Taxonomy and classification of approaches

Cited by 17 publications

References 2 publications

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

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

A Lightweight Approach to Detect Memory Leaks in JavaScript (S)

UPC: Large-Scale Memory Efficient Java Primitive Collections

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