AntTracks TrendViz

Weninger, Markus; Makor, Lukas; Gander, Elias; Mössenböck, Hanspeter

doi:10.1145/3302541.3313100

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…This can be used to analyze differences of the heap. More excitingly, this enables (1) time travel debugging [7,8,52], a powerful debugging technique that allows tracking of the sequence of program states leading to the error, and (2) identifying memory leaks [66,109,110]. Safety.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Object Graph Programming

Thimmaiah,

Lampropoulos,

Rossbach

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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We introduce Object Graph Programming (OGO), which enables reading and modifying an object graph (i.e., the entire state of the object heap) via declarative queries. OGO models the objects and their relations in the heap as an object graph thereby treating the heap as a graph database: each node in the graph is an object (e.g., an instance of a class or an instance of a metadata class) and each edge is a relation between objects (e.g., a field of one object references another object). We leverage Cypher, the most popular query language for graph databases, as OGO's query language. Unlike LINQ, which uses collections (e.g., List) as a source of data, OGO views the entire object graph as a single "collection". OGO is ideal for querying collections (just like LINQ), introspecting the runtime system state (e.g., finding all instances of a given class or accessing fields via reflection), and writing assertions that have access to the entire program state. We prototyped OGO for Java in two ways: (a) by translating an object graph into a Neo4j database on which we run Cypher queries, and (b) by implementing our own in-memory graph query engine that directly queries the object heap. We used OGO to rewrite hundreds of statements in large open-source projects into OGO queries. We report our experience and performance of our prototypes.

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Section: Discussion and Future Workmentioning

confidence: 99%

Object Graph Programming

Thimmaiah,

Lampropoulos,

Rossbach

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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“…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%

“…If a user detects a time window with suspicious memory growth, AntTracks' TrendViz view [120] allows to inspect the memory evolution during this time window in more detail. The first step is to define properties based on which the heap objects are grouped during analysis (see Figure 1b).…”

Section: Memory Growth Inspection: Evolution Over Time -Trendviz Viewmentioning

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.

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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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“…Our memory city visualization has a well-defined JSON interface to be independent of a specific data source. Yet, to make this work more tangible, we regularly refer to data imported from the memory monitoring tool AntTracks [30].…”

Section: Introductionmentioning

confidence: 99%

Memory Cities: Visualizing Heap Memory Evolution Using the Software City Metaphor

Weninger

Makor

Mössenböck

2020

2020 Working Conference on Software Visualization (VISSOFT)

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Tool support is essential to help developers in understanding the memory behavior of complex software systems. Anomalies such as memory leaks can dramatically impact application performance and can even lead to crashes. Unfortunately, most memory analysis tools lack advanced visualizations (especially of the memory evolution over time) that could facilitate developers in analyzing suspicious memory behavior.In this paper, we present Memory Cities, a technique to visualize an application's heap memory evolution over time using the software city metaphor. While this metaphor is typically used to visualize static artifacts of a software system such as class hierarchies, we use it to visualize the dynamic memory behavior of an application. In our approach, heap objects can be grouped by multiple properties such as their types or their allocation sites. The resulting object groups are visualized as buildings arranged in districts, where the size of a building corresponds to the number of heap objects or bytes it represents. Continuously updating the city over time creates the immersive feeling of an evolving city. This can be used to detect and analyze memory leaks, i.e., to search for suspicious growth behavior. Memory cities further utilize various visual attributes to ease this task. For example, they highlight strongly growing buildings using color, while making less suspicious buildings semi-transparent.We implemented memory cities as a standalone application developed in Unity, with a JSON-based interface to ensure easy data import from external tools. We show how memory cites can use data provided by AntTracks, a trace-based memory monitoring tool, and present case studies on different applications to demonstrate the tool's applicability and feasibility.

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AntTracks TrendViz

Cited by 6 publications

References 15 publications

Object Graph Programming

Object Graph Programming

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

Memory Cities: Visualizing Heap Memory Evolution Using the Software City Metaphor

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