An empirical study of Java bytecode programs

Collberg, Christian; Myles, Ginger; Stepp, Michael

doi:10.1002/spe.776

Cited by 48 publications

(44 citation statements)

References 13 publications

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“…Liebig et al also focused on the C preprocessor, undertaking a targeted empirical study to uncover the use of the preprocessor in encoding variations and variability [16]. Collberg et al [7] performed an in-depth empirical analysis of Java bytecode, computing a wide variety of metrics, including object-oriented metrics (e.g., classes per package, fields per class) and instruction-level metrics (e.g., instruction frequencies, common sequences of instructions). Baxter et al [6] looked at a combination of Java bytecode and Java source (generated from bytecode), where they focused on characterizing the distributions for a number of metrics.…”

Section: Related Workmentioning

confidence: 99%

An empirical study of PHP feature usage: a static analysis perspective

Hills

Klint

Vinju

2013

Proceedings of the 2013 International Symposium on Software Testing and Analysis

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PHP is one of the most popular languages for server-side application development. The language is highly dynamic, providing programmers with a large amount of flexibility. However, these dynamic features also have a cost, making it difficult to apply traditional static analysis techniques used in standard code analysis and transformation tools. As part of our work on creating analysis tools for PHP, we have conducted a study over a significant corpus of open-source PHP systems, looking at the sizes of actual PHP programs, which features of PHP are actually used, how often dynamic features appear, and how distributed these features are across the files that make up a PHP website. We have also looked at whether uses of these dynamic features are truly dynamic or are, in some cases, statically understandable, allowing us to identify specific patterns of use which can then be taken into account to build more precise tools. We believe this work will be of interest to creators of analysis tools for PHP, and that the methodology we present can be leveraged for other dynamic languages with similar features.

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Section: Related Workmentioning

confidence: 99%

An empirical study of PHP feature usage: a static analysis perspective

Hills

Klint

Vinju

2013

Proceedings of the 2013 International Symposium on Software Testing and Analysis

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“…We therefore again resort to both an optimistic and a pessimistic assumption. That being said, a study of real-world programs has shown that the shorthands are commonly used [16]; thus, the optimistic assumption is much closer to reality.…”

Section: Xquery For Metric Computationmentioning

confidence: 98%

Portable and accurate collection of calling-context-sensitive bytecode metrics for the Java virtual machine

Sarimbekov

Sewe

Binder

et al. 2011

Proceedings of the 9th International Conference on Principles and Practice of Programming in Java

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Calling-context profiles and dynamic metrics at the bytecode level are important for profiling, workload characterization, program comprehension, and reverse engineering. Prevailing tools for collecting calling-context profiles or dynamic bytecode metrics often provide only incomplete information or suffer from limited compatibility with standard JVMs. However, completeness and accuracy of the profiles is essential for tasks such as workload characterization, and compatibility with standard JVMs is important to ensure that complex workloads can be executed. In this paper, we present the design and implementation of JP2, a new tool that profiles both the inter-and intra-procedural control flow of workloads on standard JVMs. JP2 produces calling-context profiles preserving callsite information, as well as execution statistics at the level of individual basic blocks of code. JP2 is complemented with scripts that compute various dynamic bytecode metrics from the profiles. As a case-study and tutorial on the use of JP2, we use it for crossprofiling for an embedded Java processor.

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“…However to date, large-scale studies based on corpora such as Qualitas [30] have been restricted entirely to static analysis, e.g. [3,31].…”

Section: Related Workmentioning

confidence: 99%

JVM-hosted languages

White

Singer

2013

Proceedings of the 2013 International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machi

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The rapid adoption of non-Java JVM languages is impressive: major international corporations are staking critical parts of their software infrastructure on components built from languages such as Scala and Clojure. However with the possible exception of Scala, there has been little academic consideration and characterization of these languages to date. In this paper, we examine four nonJava JVM languages and use exploratory data analysis techniques to investigate differences in their dynamic behavior compared to Java. We analyse a variety of programs and levels of behavior to draw distinctions between the different programming languages. We briefly discuss the implications of our findings for improving the performance of JIT compilation and garbage collection on the JVM platform.

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An empirical study of Java bytecode programs

Cited by 48 publications

References 13 publications

An empirical study of PHP feature usage: a static analysis perspective

An empirical study of PHP feature usage: a static analysis perspective

Portable and accurate collection of calling-context-sensitive bytecode metrics for the Java virtual machine

JVM-hosted languages

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