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.

Dynamic updates to running programs improve development productivity and reduce downtime of long-running applications. This feature is however severely limited in current virtual machines for object-oriented languages. In particular, changes to classes often apply only to methods invoked after a class change, but not to active methods on the call stack of threads. Additionally, adding and removing methods as well as fields is often not supported. We present a novel programming model for safe and atomic code updates of Java programs that also updates methods that are currently executed. We introduce safe update regions and pause threads only there before an update. We automatically convert the stack frames to suit the new versions of the methods. Our implementation is based on a production-quality Java virtual machine. Additionally, we present SafeWeave, a dynamic aspect-oriented programming system that exposes the atomic code updates through a high-level programming model. AspectJ advice can be added to and removed from a running application.Changes are atomic and correctness is guaranteed even though weaving happens in parallel to program execution, and the system fully supports the dynamic class loading of Java. We show that the enhanced evolution features do not incur any performance penalty before and after version changes.

Originally conceived as the target platform for Java alone, the Java Virtual Machine (JVM) has since been targeted by other languages, one of which is Scala. This trend, however, is not yet reflected by the benchmark suites commonly used in JVM research. In this paper, we thus present the design and analysis of the first full-fledged benchmark suite for Scala. We furthermore compare the benchmarks contained therein with those from the well-known DaCapo 9.12 benchmark suite and show where the differences are between Scala and Java code-and where not.

JavaScript is the most popular programming language for client-side Web applications, and Node.js has popularized the language for server-side computing, too. In this domain, the minimal support for parallel programming remains however a major limitation. In this paper we introduce a novel parallel programming abstraction called Generic Messages (GEMS). GEMS allow one to combine message passing and shared-memory parallelism, extending the classes of parallel applications that can be built with Node.js. GEMS have customizable semantics and enable several forms of thread safety, isolation, and concurrency control. GEMS are designed as convenient JavaScript abstractions that expose high-level and safe parallelism models to the developer. Experiments show that GEMS outperform equivalent Node.js applications thanks to their usage of shared memory.