Java Bytecode Instrumentation Made Easy: The DiSL Framework for Dynamic Program Analysis

Marek, Lukáš; Zheng, Yudi; Ansaloni, Danilo; Sarimbekov, Aibek; Binder, Walter; Tůma, Petr; Qi, Zhengwei

doi:10.1007/978-3-642-35182-2_18

Cited by 8 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous work, we presented the design and the basic features of the DiSL framework [22,23,24]. Here we extend our previous work in the following directions:…”

Section: Introductionmentioning

confidence: 64%

“…Various isolated aspects of the DiSL framework were presented in previous work [22,23,24]. This paper extends our previous presentation of DiSL-we provide an overview of the DiSL architecture, introduce the DiSL programming model and illustrate the basic concepts with a running example.…”

mentioning

confidence: 62%

“…To reconcile the convenience of the pointcut/advice model found in AOP and the expressiveness and performance attainable by using the low-level bytecode manipulation libraries, we have previously presented DiSL [22,23,24], an open-source framework that enables rapid development of efficient instrumentations for Java-based DPA tools. To raise the level of abstraction, DiSL adopts the AOP-based pointcut/advice model, which allows one to express instrumentations in a very concise manner, similar to AOP aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The rest of the paper is structured as follows: Section 2 stems from [24] and introduces the DiSL framework using a simple execution time profiler as a running example, pointing out the advantages of using DiSL to implement the instrumentation. Section 3 provides an overview of the advanced features of DiSL, which also serve as extension points of the DiSL framework.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Introduction to dynamic program analysis with DiSL

Marek

Zheng

Ansaloni

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

Self Cite

View full text Add to dashboard Cite

Dynamic program analysis (DPA) tools assist in many software engineering and development tasks, including profiling, program comprehension, and performance model construction and calibration. On the Java platform, many DPA tools are implemented either using aspect-oriented programming (AOP), or rely on bytecode instrumentation to modify the base program code. The pointcut/advice model found in AOP enables rapid tool development, but does not allow expressing certain instrumentations due to limitations of mainstream AOP languages-developers thus use bytecode manipulation to gain more expressiveness and performance. However, while the existing bytecode manipulation libraries handle some low-level details, they still make tool development tedious and error-prone. Targeting this issue, we provide the first complete presentation of DiSL, an open-source instrumentation framework that reconciles the conciseness of the AOP pointcut/advice model and the expressiveness and performance achievable with bytecode manipulation libraries. Specifically, we extend our previous work to provide an overview of the DiSL architecture, advanced features, and the programming model. We also include case studies illustrating successful deployment of DiSL-based DPA tools.

show abstract

“…In previous work, we presented the design and the basic features of the DiSL framework [22,23,24]. Here we extend our previous work in the following directions:…”

Section: Introductionmentioning

confidence: 64%

mentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Introduction to dynamic program analysis with DiSL

Marek

Zheng

Ansaloni

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Javabased systems, byte code instrumentation is more popular than source code instrumentation. There are many frameworks providing instrumenting functionalities (e.g., DiSL [21], InsECT [6,26], jCello [27], and BCEL [2]) for Java. These are very similar to each other regarding their provided functionalities.…”

Section: Related Workmentioning

confidence: 99%

Code Coverage Measurement Framework for Android Devices

Horváth¹,

Bognár²,

Gergely³

et al. 2014

Acta Cybern

View full text Add to dashboard Cite

Software testing is a very important activity in the software development life cycle. Numerous general black-and white-box techniques exist to achieve different goals and there are a lot of practices for different kinds of software. The testing of embedded systems, however, raises some very special constraints and requirements in software testing. Special solutions exist in this field, but there is no general testing methodology for embedded systems. One of the goals of the CIRENE project was to fill this gap and define a general testing methodology for embedded systems that could be specialized to different environments. The project included a pilot implementation of this methodology in a specific environment: an Android-based Digital TV receiver (Set-Top-Box). In this pilot, we implemented method level code coverage measurement of Android applications. This was done by instrumenting the applications and creating a framework for the Android device that collected basic information from the instrumented applications and communicated it through the network towards a server where the data was finally processed. The resulting code coverage information was used for many purposes according to the methodology: test case selection and prioritization, traceability computation, dead code detection, etc. The resulting methodology and toolset were reused in another project where we investigated whether the coverage information can be used to determine locations to be instrumented in order to collect relevant information about software usability. In this paper, we introduce the pilot implementation and, as a proof-ofconcept, present how the coverage results were used for different purposes.

show abstract

Large‐scale characterization of Java streams

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Java Bytecode Instrumentation Made Easy: The DiSL Framework for Dynamic Program Analysis

Cited by 8 publications

References 11 publications

Introduction to dynamic program analysis with DiSL

Introduction to dynamic program analysis with DiSL

Code Coverage Measurement Framework for Android Devices

Large‐scale characterization of Java streams

Contact Info

Product

Resources

About