This paper addresses the problem of resolving virtual method and interface calls in Java bytecode. The main focus is on a new practical technique that can be used to analyze large applications. Our fundamental design goal was to develop a technique that can be solved with only one iteration, and thus scales linearly with the size of the program, while at the same time providing more accurate results than two popular existing linear techniques, class hierarchy analysis and rapid type analysis. We present two variations of our new technique, variable-type analysis and a coarser-grain version called declared-type analysis. Both of these analyses are inexpensive, easy to implement, and our experimental results show that they scale linearly in the size of the program. We have implemented our new analyses using the Soot framework, and we report on empirical results for seven benchmarks. We have used our techniques to build accurate call graphs for complete applications (including librarie...

Efficient invalidation and dynamic replacement of executing code – on-stack replacement (OSR), is necessary to facilitate effective, aggressive, specialization of object-oriented programs that are dynamically loaded, incrementally compiled, and garbage collected. Extant OSR mechanisms restrict the performance potential of program specialization since their implementations are special-purpose and restrict compiler optimization. In this paper, we present a novel, general-purpose OSR mechanism that is more amenable to optimization than prior approaches. In particular, we decouple the OSR implementation from the optimization process and update the program state information incrementally during optimization. Our OSR implementation efficiently enables the use of code specializations that are invalidated by any event – including those external to program code execution. We improve code quality over the extant, state-of-the-art, resulting in performance gains of 1-31%, and 9 % on average.

In this paper, we describe a novel execution environment that can dynamically switch between garbage collection systems. As such, it enables selection of the most appropriate allocator and collector for a given application and underlying resource availability. Our system is novel in that it is able to switch between a wide range of diverse collection systems. It uses program annotations to guide selection of the collection system. In addition, it can automatically identify when to switch collectors when program execution behavior warrants it, i.e., it is adaptive. Our system introduces little overhead and accurately identifies the best collector for a wide range of benchmarks and heap sizes.