Flexible and efficient profiling with aspect‐oriented programming

Abstract

SUMMARYMany 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.

Introduction to dynamic program analysis with DiSL

“…In contrast, an instrumentation framework such as DiSL has no need for the usual form of the around advice, which lets the advice code decide whether to skip or proceed with the method invocation [22]. DiSL therefore only supports inlining snippets before and after a particular join point, together with a way for the snippets inlined into the same method to exchange data using synthetic local variables [26], as illustrated on Figure 2.…”

Aspectizing JavaScript security

“…AOP has, for instance, been applied with some success to profiling [6] and runtime verification [1]. However, different domains have different needs, in particular in terms of the join-point model and, unfortunately, tracking information flows requires to operate at a granularity level not covered by our off-the-shelf weaver for JavaScript, AspectScript.…”

Challenges for Refinement and Composition of Instrumentations: Position Paper

“…Thus, later instrumentations generally cannot distinguish between the original code and code inserted by earlier instrumentations. 4 As a consequence, controlling the composition of instrumentations becomes infeasible. This is also true of the Scala library Mnemonics [11] which is slightly less low-level than the aforementioned frameworks; by exploiting Scala's type system, it ensures that only certain well-formed, type-safe bytecode sequences can be generated.…”

“…In the past, the AspectJ language [8] (or derivatives) has been used for implementing dynamic analyses; often alternative compilers are used for this purpose, such as MAJOR [4,16,17] and MAJOR2 [10,13] which allows instrumenting the Java class library unlike the standard AspectJ weaver. In AspectJ, aspects extend classes with pointcuts and advice.…”