PerformanceHat

Cito, Jürgen; Leitner, Philipp; Bosshard, Christian; Knecht, Markus; Mazlami, Genc; Gall, Harald C.

doi:10.1145/3183440.3183481

Cited by 21 publications

(3 citation statements)

References 9 publications

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“…Therefore, detecting performance problems has been of interest among researchers (Velez et al, 2021), from researching Machine Learning methods for performance prediction (Kaltenecker et al, 2020) to the creation of several tools to aid in detecting performance issues earlier on. Some of the available tools are: (i) Per-formanceHat (Cito et al, 2018) (This tool aids to bring awareness to development choices and its impact on performance.) and (ii) Toddler (Nistor et al, 2013) (An automated oracle to detect redundant and inefficient use of loops, causing unnecessary performance degradation.).…”

Section: From Automatic Detection Of Performance Issues To Cps-pasmentioning

confidence: 99%

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

Dinten

Derakhshanfar

Panichella

et al. 2023

Preprint

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Section: From Automatic Detection Of Performance Issues To Cps-pasmentioning

confidence: 99%

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

Dinten

Derakhshanfar

Panichella

et al. 2023

Preprint

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“…Yet, it is this development environment where the user is expected to fix the just detected memory anomaly, however, without further guidance. Developing an IDE plugin [4,21] (and probably even expanding the tool's capabilities by the use of hybrid static and dynamic analysis [31]) would make it possible to automatically detect and highlight suspicious code segments and provide further guidance on the source code level.…”

Section: Recommendationsmentioning

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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“…To this intent, one of the major challenges is to work on achieving a more efficient integration between the design and runtime aspects of systems. For instance, through observation and instrumentation, logs and metrics can be collected and related to the original software design in order to comprehend, extrapolate and analyze the inner behavior of a running software system [3].…”

Section: Introductionmentioning

confidence: 99%

A Model-driven Approach for Continuous Performance Engineering in Microservice-based Systems

Cortellessa,

Di Pompeo,

Eramo

et al. 2023

Preprint

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Microservices are quite widely impacting on the software industry in recent years. Rapid evolution and continuous deployment represent specific benefits of microservice-based systems, but they may have a significant impact on nonfunctional properties like performance. Despite the obvious relevance of this property, there is still a lack of systematic approaches that explicitly take into account performance issues in the lifecycle of microservice-based systems.In such a context of evolution and re-deployment, Model-Driven Engineering techniques can provide major support to various software engineering activities, and in particular they can allow managing the relationships between a running system and its architectural model.In this paper, we propose a model-driven integrated approach that exploits traceability relationships between the monitored data of a microservice-based running system and its architectural model to derive recommended refactoring actions that lead to performance improvement. The approach has been applied and validated on two microservice-based systems, in the domain of e-commerce and ticket reservation, respectively, whose architectural models have been designed in UML profiled with MARTE.

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PerformanceHat

Cited by 21 publications

References 9 publications

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

The Slow and the Furious? Performance Antipattern Detection in Cyber-Physical Systems

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

A Model-driven Approach for Continuous Performance Engineering in Microservice-based Systems

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