ShadowVM

Marek, Lukáš; Kell, Stephen; Zheng, Yudi; Bulej, Lubomír; Binder, Walter; Tůma, Petr; Ansaloni, Danilo; Sarimbekov, Aibek; Sewe, Andreas

doi:10.1145/2637365.2517219

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…To further isolate the analysis from the execution of the application, Stream‐Analyzer uses Shadow VM , 41 a deployment setting of DiSL which allows running analyses in a separate JVM process. This feature helps mitigate the perturbations incurred by the inserted instrumentation code while also prevents known issues inherent to non‐isolated approaches 38 .…”

Section: Methodsmentioning

confidence: 99%

Large‐scale characterization of Java streams

et al. 2023

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Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators.

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Section: Methodsmentioning

confidence: 99%

Large‐scale characterization of Java streams

et al. 2023

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“…More flexible analysis frameworks have been presented, such as ShadowVM [38] for Java and Android, which offers advanced analysis isolation and coverage features. It allows for the execution of multiple analysis tools simultaneously and asynchronously.…”

Section: B Limitationsmentioning

confidence: 99%

Dynamic Program Analysis with Flexible Instrumentation and Complex Event Processing

Soueidi,

Falcone,

Hallé

2023

2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)

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This paper presents a flexible and modular approach to dynamic program analysis for JVM-based languages, aiming to address the limitations of existing tools, in particular their limited expressivity and tight coupling between instrumentation and analysis. The proposed solution decouples these two processes using BISM, a lightweight instrumentation language, and BeepBeep, a complex event processing engine. This novel combination enhances expressiveness, promotes reusability, and integrates seamlessly into JVM-based projects. Various analyses such as monitoring, profiling, coverage measurement, and complex event generation are demonstrated, showcasing the approach's flexibility.

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“…Other profiling solutions exists in the literature, although they center the analysis in improving the speed of the running application. So, they provide little to none information about the interactions between the VM components [21,25,33,38].…”

Section: Related Workmentioning

confidence: 99%

Profiling code cache behaviour via events

Tesone

Polito

Ducasse

2021

Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

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Virtual machine performance tuning for a given application is an arduous and challenging task. For example, parametrizing the behaviour of the JIT compiler machine code caches affects the overall performance of applications while being rather obscure for final users not knowledgeable about VM internals. Moreover, VM components are often heavily coupled and changes in some parameters may affect several seemingly unrelated components and may have unclear performance impacts. Therefore, choosing the best parametrization requires to have precise information.In this paper, we present Vicoca, a tool that allows VM users and developers to obtain detailed information about the behaviour of the code caches and their interactions with other virtual machine components. We present a complex optimization problem due to the heavy interaction of components in the Pharo VM, and we explain it using Vicoca. The information produced by the tool allows developers to produce an optimized configuration for the VM. Vicoca is based on event recording that are manipulated during offline analysis. Vicoca not only allows us to understand this given problem, but it opens the door to future work such as automatic detection of application characteristics, identification of performance issues, and automatic hinting.CCS Concepts: • Software and its engineering → Runtime environments.

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ShadowVM

Cited by 8 publications

References 27 publications

Large‐scale characterization of Java streams

Large‐scale characterization of Java streams

Dynamic Program Analysis with Flexible Instrumentation and Complex Event Processing

Profiling code cache behaviour via events

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