Logo des Repositoriums

Softwaretechnik-Trends 40(3) - 2020

https://dl.gi.de/handle/20.500.12116/39779
Auflistung nach:

Auflistung Softwaretechnik-Trends 40(3) - 2020 nach Schlagwort "AntTracks"

1 - 2 von 2
  • Konferenzbeitrag
    Heap Evolution Analysis Using Tree Visualizations
    (Softwaretechnik-Trends Band 40, Heft 3, 2020) Weninger, Markus; Makor, Lukas; Mössenböck, Hanspeter
    Memory anomalies such as memory leaks can dramatically impact application performance and can even lead to crashes. Thus, supporting developers in understanding the heap memory behavior of their systems is essential. Unfortunately, most memory analysis tools lack advanced visualizations that could facilitate developers in analyzing suspicious memory behavior. To analyze heap memory, it is common to group the heap’s objects, for example, by their types or by their allocation sites. Using multiple grouping criteria thus results in a tree-shaped representation of the heap content. Such a heap tree is then typically presented textually in a tree table. In this paper, we present ongoing research on using well-known tree visualization techniques to visualize such heap trees as well as their evolution over time. Such visualizations may ease the detection of proliferating heap objects, facilitating memory leak analysis. To demonstrate the feasibility and applicability of the presented approach, we implemented a web-based visualization tool and integrated it into AntTracks, our trace-based memory monitoring tool.
  • Konferenzbeitrag
    Investigating High Memory Churn via Object Lifetime Analysis to Improve Software Performance
    (Softwaretechnik-Trends Band 40, Heft 3, 2020) Weninger, Markus; Gander, Elias; Mössenböck, Hanspeter
    High memory churn occurs when many temporary objects are created and shortly thereafter collected by the garbage collector. Such excessive dynamic allocations negatively impact an application’s performance because (1) a great number of objects has to be allocated on the heap and (2) an increased number of garbage collections is required to collect them. In this paper, we present ongoing research on how to support developers in detecting, understanding and resolving high memory churn in order to improve their application’s performance. Based on a recorded memory trace, an algorithm automatically searches for memory churn hotspots and calculates the age at which objects die within it, since objects that die young are the major contributors to memory churn. Information about these objects, for example their types and allocation sites, can then be inspected in order to locate the problematic code locations. To demonstrate the feasibility and applicability of our approach, we implemented and present a new memory churn analysis feature in AntTracks, our trace-based memory monitoring tool.