Summary Bytecode instrumentation is a widely used technique to implement aspect weaving and dynamic analyses in virtual machines such as the Java virtual machine. Aspect weavers and other instrumentations are usually developed independently and combining them often requires significant engineering effort, if at all possible. In this article, we present polymorphic bytecode instrumentation(PBI), a simple but effective technique that allows dynamic dispatch amongst several, possibly independent instrumentations. PBI enables complete bytecode coverage, that is, any method with a bytecode representation can be instrumented. We illustrate further benefits of PBI with three case studies. First, we describe how PBI can be used to implement a comprehensive profiler of inter‐procedural and intra‐procedural control flow. Second, we provide an implementation of execution levels for AspectJ, which avoids infinite regression and unwanted interference between aspects. Third, we present a framework for adaptive dynamic analysis, where the analysis to be performed can be changed at runtime by the user. We assess the overhead introduced by PBI and provide thorough performance evaluations of PBI in all three case studies. We show that pure Java profilers like JP2 can, thanks to PBI, produce accurate execution profiles by covering all code, including the core Java libraries. We then demonstrate that PBI‐based execution levels are much faster than control flow pointcuts to avoid interference between aspects and that their efficient integration in a practical aspect language is possible. Finally, we report that PBI enables adaptive dynamic analysis tools that are more reactive to user inputs than existing tools that rely on dynamic aspect‐oriented programming with runtime weaving. These experiments position PBI as a widely applicable and practical approach for combining bytecode instrumentations. © 2015 The Authors. Software: Practice and Experience Published by John Wiley & Sons Ltd.

Modern IDEs such as Eclipse offer static views of the source code, but such views ignore information about the runtime behavior of software systems. Since typical object-oriented systems make heavy use of polymorphism and dynamic binding, static views will miss key information about the runtime architecture. In this paper, we present an approach to gather and integrate dynamic information in the Eclipse IDE with the goal of better supporting typical software maintenance activities. By means of a controlled experiment with 30 professional developers, we show that for typical software maintenance tasks, integrating dynamic information into the Eclipse IDE yields a significant 17.5 percent decrease of time spent while significantly increasing the correctness of the solutions by 33.5 percent. We also provide a comprehensive performance evaluation of our approach.

Abstract Many profilers for virtual execution environments, such as the Java virtual machine (JVM), are implemented with low‐level bytecode instrumentation techniques, which is tedious, error‐prone, and complicates maintenance and extension of the tools. In order to reduce the development time and cost, we promote building profilers for the JVM using high‐level aspect‐oriented programming (AOP). We show that the use of aspects yields concise profilers that are easy to develop, extend, and maintain, because low‐level instrumentation details are hidden from the tool developer. In order to build efficient profilers, we introduce inter‐advice communication, an extension to common AOP languages that enables efficient data passing between advices that are woven into the same method using local variables. We illustrate our approach with two case studies. First, we show that an existing, instrumentation‐based tool for listener latency profiling can be easily recast as an aspect. Second, we present an aspect for comprehensive calling context profiling. In order to reduce profiling overhead, our aspect parallelizes application execution and profile creation, resulting in a speedup of 110% on a machine with more than two cores, compared with a primitive, non‐parallel approach. Copyright © 2011 John Wiley & Sons, Ltd.

Aspect-oriented programming provides a convenient high-level model to define several kinds of dynamic analyses, in particular thanks to recent advances in exhaustive weaving in core libraries. Casting dynamic analyses as aspects allows the use of a single weaving infrastructure to apply different analyses to the same base program, simultaneously. However, even if dynamic analysis aspects are mutually independent, their mere presence perturbates the observations of others: this is due to the fact that aspectual computation is potentially visible to all aspects. Because current aspect composition approaches do not address this kind of computational interference, combining different analysis aspects yields at best unpredictable results. It is also impossible to flexibly combine various analyses, for instance to analyze an analysis aspect. In this paper we show how the notion of execution levels makes it possible to effectively address these composition issues. In order to realize this approach, we explore the practical and efficient integration of execution levels in a mainstream aspect language, AspectJ. We report on a case study of composing two out-of-the-box analysis aspects in a variety of ways, highlighting the benefits of the approach.

While dynamic code evolution in object-oriented systems is an important feature supported by dynamic languages, there is currently only limited support for dynamic code evolution in high-performance, state-of-the-art runtime systems for statically typed languages, such as the Java Virtual Machine. In this tool demonstration, we present the Dynamic Code Evolution VM, which is based on a recent version of Oracle's state-of-the-art Java HotSpot(TM) VM and allows unlimited changes to loaded classes at runtime. Based on the Dynamic Code Evolution VM, we developed an enhanced version of the Mantisse GUI builder (which is part of the NetBeans IDE) that allows adding GUI components without restarting the application under development. Furthermore, we redesigned the dynamic AOP framework HotWave to take advantage of the enhanced dynamic code evolution capabilities. The new version, HotWave2, now supports most AspectJ constructs, including around() advice and static cross-cutting. We will demonstrate both the enhanced Mantisse GUI builder as well as HotWave2, weaving several aspects for dynamic analysis in sizable applications at runtime.

Abstract Calling context profiling is an important technique for analyzing the performance of object‐oriented software with complex inter‐procedural control flow. The Calling Context Tree (CCT) is a common data structure that stores dynamic metrics, such as CPU time, separately for each calling context. As CCTs may comprise millions of nodes, there is a need for a condensed visualization that eases the localization of performance bottlenecks. In this article, we discussCalling Context Ring Charts(CCRCs), a compact visualization for CCTs, where callee methods are represented in ring segments surrounding the caller's ring segment. In order to reveal hot methods, their callers, and callees, the ring segments can be sized according to a chosen dynamic metric. We describe two case studies where CCRCs help us to detect and fix performance problems in applications. A performance evaluation also confirms that our implementation can efficiently handle large CCTs. Copyright © 2010 John Wiley & Sons, Ltd.

Dynamic aspect-oriented programming (AOP) enables runtime adaptation of aspects, which is important for building sophisticated, aspect-based software engineering tools, such as adaptive profilers or debuggers that dynamically modify instrumentation code in response to user interactions. Today, many AOP frameworks for Java, notably AspectJ, focus on aspect weaving at compile-time or at load-time, and offer only limited support for aspect adaptation and reweaving at runtime. In this paper, we introduce HotWave, an AOP framework based on AspectJ for standard Java Virtual Machines (JVMs). HotWave supports dynamic (re)weaving of previously loaded classes, and it ensures that all classes loaded in a JVM can be (re)woven, including the classes of the standard Java class library. HotWave features a novel mechanism for inter-advice communication, enabling efficient data passing between advices that are woven into the same method. We explain HotWave's programming model and discuss our implementation techniques. As case study, we present an adaptive, aspect-based profiler that leverages HotWave's distinguishing features.

Developing tools for profiling, debugging, testing, and reverse engineering is error-prone, time-consuming, and therefore costly when using low-level techniques, such as bytecode instrumentation. As a solution to these problems, we promote tool development in Java using high-level aspect-oriented programming (AOP). We demonstrate that the use of aspects yields compact tools that are easy to develop and extend. As enabling technology, we rely on HotWave, a new tool for dynamic and comprehensive aspect weaving. HotWave reconciles compatibility with existing virtual machine and AOP technologies. It provides support for runtime adaptation of aspects and reweaving of previously loaded code, as well as the ability to weave aspects into all methods executing in a Java Virtual Machine, including methods in the standard Java class library. HotWave also features a new mechanism for efficiently passing data between advices that are woven into the same method. We demonstrate the benefits of HotWave's distinguishing features with two case studies in the area of profiling.

Mainstream IDEs such as Eclipse support developers in managing software projects mainly by offering static views of the source code. Such a static perspective neglects any information about runtime behavior. However, object-oriented programs heavily rely on polymorphism and late-binding, which makes them difficult to understand just based on their static structure. Developers thus resort to debuggers or profilers to study the system's dynamics. However, the information provided by these tools is volatile and hence cannot be exploited to ease the navigation of the source space. In this paper we present an approach to augment the static source perspective with dynamic metrics such as precise runtime type information, or memory and object allocation statistics. Dynamic metrics can leverage the understanding for the behavior and structure of a system. We rely on dynamic data gathering based on aspects to analyze running Java systems. By solving concrete use cases we illustrate how dynamic metrics directly available in the IDE are useful. We also comprehensively report on the efficiency of our approach to gather dynamic metrics.

Maintaining object-oriented systems that use inheritance and polymorphism is difficult, since runtime information, such as which methods are actually invoked at a call site, is not visible in the static source code. We have implemented Senseo, an Eclipse plugin enhancing Eclipse's static source views with various dynamic metrics, such as runtime types, the number of objects created, or the amount of memory allocated in particular methods.

Developing and maintaining tools for profiling, debugging, testing, and reverse engineering can be difficult when using low-level techniques, such as bytecode instrumentation. We promote tool development in Java using high-level aspect-oriented programming. We demonstrate that the use of aspects yields concise tools that are easy to develop, extend, and maintain, because low-level instrumentation details are hidden from the developer. We introduce MAJOR, a new tool for comprehensive aspect weaving, which ensures that aspects are woven into all classes executing in a Java Virtual Machine, including those in the standard Java class library. MAJOR includes the pluggable module CARAJillo, which supports efficient access to a complete and customizable calling context representation. Both distinguishing features of MAJOR - comprehensive aspect weaving and efficient access to complete calling information - are essential in the aforementioned domains.

Calling context profiling is an important technique for locating hotspots in programs. The prevailing data structure is the Calling Context Tree (CCT) that provides dynamic metrics for each calling context. Existing approaches to calling context profiling in Java either limit portability due to the use of native code or of a modified Java Virtual Machine, create incomplete and inaccurate CCTs, or cause excessive overhead. In this paper, we introduce Complete Calling Context Profiling (CCCP), a new approach that reconciles completeness and accuracy of the created CCTs, portability, and moderate overhead. CCCP relies on a generic bytecode instrumentation framework ensuring comprehensive bytecode coverage, including also the standard Java class library. In order to reduce the overhead of accessing the current CCT node, CCCP transforms code such that the caller passes its CCT node to the callee as a special method argument, while ensuring compatibility with native code, reflection, and stack introspection. We use the resulting CCTs for a detailed analysis of the dynamic behavior of Java systems and present a thorough analysis of the origin of runtime overheads.

Performance evaluation of embedded software is essential in an early development phase so as to ensure that the software will run on the embedded device's limited computing resources. Prevailing approaches either require the deployment of the software on the embedded target, which can be tedious and may be impossible in an early development phase, or rely on simulation, which can be very slow. In this paper we present CProf, a customizable cross-profiler for embedded Java processors. It allows the developer to profile the embedded software in the host environment, completely decoupled from the target system, in a standard Java Virtual Machine, but the generated profiles represent the execution time metric of the target system. CProf enables calling context cross-profiling and customizable online processing of profiling data. Furthermore, it supports pluggable CPU cycle estimation and cache simulation strategies, easing the reconfiguration for different target processors. Using the Java Optimized Processor JOP as target, CProf's cross-profiles have only a small percent error below 3.3%.

Abstract Virtual execution environments, such as the Java virtual machine, promote platform‐independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform‐specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform‐dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling‐context‐sensitive program execution statistics. We explore the use of platform‐independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java‐based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Performance evaluation of embedded software is essential in an early development phase so as to ensure that the software will run on the embedded device's limited computing resources. The prevailing approaches either require the deployment of the software on the embedded target, which can be tedious and may be impossible in an early development phase, or rely on simulation, which can be very slow. In this article, we introduce a customizable cross‐profiling framework for embedded Java processors, including processors featuring a method cache. The developer profiles the embedded software in the host environment, completely decoupled from the target system, on any standard Java virtual machine, but the generated profiles represent the execution time metric of the target system. Our cross‐profiling framework is based on bytecode instrumentation. We identify several pointcuts in the execution of bytecode that need to be instrumented in order to estimate the CPU cycle consumption on the target system. An evaluation using the JOP embedded Java processor as target confirms that our approach reconciles high profile accuracy with moderate overhead. Our cross‐profiling framework also enables the performance evaluation of new processor architectures before they are implemented. As a case study, we explore the performance impact of various processor design choices and optimizations, such as different cache sizes or pipeline organizations, and come up with an improved processor design that yields speedups of up to 40% on standard Java benchmarks. Copyright © 2009 John Wiley & Sons, Ltd.

Performance evaluation of embedded software is essential in an early development phase so as to ensure that the software will run on the embedded device's limited computing resources. Prevailing approaches either require the deployment of the software on the embedded target, which can be tedious and may be impossible in an early development phase, or rely on simulation, which can be very slow. In this paper, we introduce a customizable cross-profiling framework for embedded Java processors, including processors featuring a method cache. The developer profiles the embedded software in the host environment, completely decoupled from the target system, on any standard Java Virtual Machine, but the generated profiles represent the execution time metric of the target system. Our cross-profiling framework is based on bytecode instrumentation. We identify several pointcuts in the execution of bytecode that need to be instrumented in order to estimate the CPU cycle consumption on the target system. An evaluation using the JOP embedded Java processor as target confirms that our approach reconciles high profile accuracy with moderate overhead. Our cross-profiling framework also enables the rapid evaluation of the performance impact of possible optimizations, such as different caching strategies.