JP2: Call-site aware calling context profiling for the Java Virtual Machine

Sarimbekov, Aibek; Sewe, Andreas; Binder, Walter; Moret, Philippe; Mezini, Mira

doi:10.1016/j.scico.2011.11.003

Cited by 24 publications

(21 citation statements)

References 15 publications

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“…To simplify development of general-purpose benchmark suites, we provide generic analyses as a part of our toolchain, consisting of a mix of static (e.g., code complexity analyzer and source code counter) and dynamic analyses (e.g., code coverage analyzer and calling-context profiler JP2 [10]). …”

Section: Characterizing Workloads To Facilitate Selectionmentioning

confidence: 99%

AutoBench: Finding Workloads That You Need Using Pluggable Hybrid Analyses

Zheng

Rosà

Salucci

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Researchers often rely on benchmarks to demonstrate feasibility or efficiency of their contributions. However, finding the right benchmark suite can be a daunting taskexisting benchmark suites may be outdated, known to be flawed, or simply irrelevant for the proposed approach. Creating a proper benchmark suite is challenging, extremely time consuming, and also-unless it becomes widely popular-a thankless endeavor. In this paper, we introduce a novel approach to help researchers find relevant workloads for their experimental evaluation needs. Our approach relies on the huge number of open-source projects available in public repositories, and on unit testing having become best practice in software development. Using a repository crawler employing pluggable static and dynamic analyses for filtering and workload characterization, we allow users to automatically find projects with relevant workloads. Preliminary results presented here show that unit tests can provide a viable source of workloads, and that the combination of static and dynamic analyses improves the ability to identify relevant workloads that can serve as the basis for custom benchmark suites.

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Section: Characterizing Workloads To Facilitate Selectionmentioning

confidence: 99%

AutoBench: Finding Workloads That You Need Using Pluggable Hybrid Analyses

Zheng

Rosà

Salucci

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Any number and type of platform-independent (execution count of methods, number of executed bytecodes) or platform-dependent (CPU time, number of cache misses) dynamic metrics can be kept in a CCT node. In our approach, we evaluate the dynamic metrics from CCTs generated using a calling context profiler, JP2 [25].…”

Section: ) Calling Context Tree (Cct)mentioning

confidence: 99%

“…We used an open source tool JP2 [25] to produce CCT profiles for our evaluation. JP2 is a calling context profiler for the Java Virtual Machine, which collects complete and accurate profiles.…”

Section: Tool Used -Jp2mentioning

confidence: 99%

Efficient and Scalable Collection of Dynamic Metrics Using MapReduce

Sarvari

Singh

Sikka

2015

2015 Asia-Pacific Software Engineering Conference (APSEC)

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Dynamic metrics are known to assess the actual behavior of software systems as they are extracted from runtime data obtained during program execution. However, recent literature indicates that dealing with dynamic information remains a formidable challenge due to the huge size of execution data at hand, resulting in long processing delays. We present an efficient and scalable technique to extract design level dynamic metrics from Calling Context Tree (CCT) using cloud based MapReduce paradigm. CCT profiles having node count up to 40 million are used to extract a number of dynamic coupling metrics. On an average, 73% increase in performance is observed as compared to sequential analysis. Also other performance characteristics like speed-up and scale-up are analyzed to strengthen the applicability of our parallel computation approach.

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“…JP2 [26] is a calling-context profiler for Java. For each method, JP2 collects various static metrics (i.e., method names, number and sizes of basic blocks) and dynamic metrics (i.e., method invocations, basic block executions, and number of executed bytecodes), and associates them with a corresponding node in a calling-context tree (CCT) [27], grouped by the position of the method call-site in the caller.…”

Section: Overview Of Recasted Analysis Toolsmentioning

confidence: 99%

Dynamic program analysis—Reconciling developer productivity and tool performance

Sarimbekov

Zheng

Ansaloni

et al. 2014

Science of Computer Programming

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Dynamic program analysis tools serve many important software engineering tasks such as profiling, debugging, testing, program comprehension, and reverse engineering. Many dynamic analysis tools rely on program instrumentation and are implemented using low-level instrumentation libraries, resulting in tedious and error-prone tool development. Targeting this issue, we have created the Domain-Specific Language for Instrumentation (DiSL), which offers high-level programming abstractions especially designed for instrumentation-based dynamic analysis. When designing DiSL, our goal was to boost the productivity of tool developers targeting the Java Virtual Machine, without impairing the performance of the resulting tools. In this paper we assess whether DiSL meets this goal. First, we perform a controlled experiment to measure tool development time and correctness of the developed tools, comparing DiSL with a prevailing, state-of-the-art instrumentation library. Second, we recast 10 open-source software engineering tools in DiSL and compare source code metrics and performance with the original implementations. Our studies show that DiSL significantly improves developer productivity, enables concise tool implementations, and does not have any negative impact on tool performance.

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JP2: Call-site aware calling context profiling for the Java Virtual Machine

Cited by 24 publications

References 15 publications

AutoBench: Finding Workloads That You Need Using Pluggable Hybrid Analyses

AutoBench: Finding Workloads That You Need Using Pluggable Hybrid Analyses

Efficient and Scalable Collection of Dynamic Metrics Using MapReduce

Dynamic program analysis—Reconciling developer productivity and tool performance

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