Many opportunities for easy, big-win, program optimizations are missed by compilers. This is especially true in highly layered Java applications. Often at the heart of these missed optimization opportunities lie computations that, with great expense, produce data values that have little impact on the program’s final output. Constructing a new date formatter to format every date, or populating a large set full of expensively constructed structures only to check its size: these involve costs that are out of line with the benefits gained. This disparity between the formation costs and accrued benefits of data structures is at the heart of much runtime bloat. We introduce a run-time analysis to discover these low-utility data structures. The analysis employs dynamic thin slicing, which naturally associates costs with value flows rather than raw data flows. It constructs a model of the incremental, hop-to-hop, costs

In large programs written in managed languages such as Java and C#, holding unnecessary references often results in memory leaks and bloat, degrading significantly their run-time performance and scalability. Despite the existence of many leak detectors for such languages, these detectors often target low-level objects; as a result, their reports contain many false warnings and lack sufficient semantic information to help diagnose problems. This paper introduces a specification-based technique called LeakChaser that can not only capture precisely the unnecessary references leading to leaks, but also explain, with high-level semantics, why these references become unnecessary. At the heart of LeakChaser is a three-tier approach that uses varying levels of abstraction to assist programmers with different skill levels and code familiarity to find leaks. At the highest tier

Abstract. As a culture, object-orientation encourages programmers to create objects, both short- and long-lived, without concern for cost. Excessive object creation and initialization can cause severe runtime bloat, which degrades significantly application performance and scalability. A frequently-occurring coding pattern that may lead to large volumes of (temporary) objects is the creation of objects that, while allocated per loop iteration, contain values independent of specific iterations. Finding these objects and moving them out of loops requires sophisticated interprocedural analysis, a task that is difficult for traditional dataflow analyses such as loop-invariant code motion to accomplish. Our work targets data structures that are loop-invariant, and presents a static type and effect system to detect loop-invariant data structures. For each loop, our analysis inspects each logical data structure in order to find those that have disjoint instances per loop iteration and contain loop-invariant data. Instead of automatically hoisting them to improve performance (which is over-conservative), we report hoistability measurements for each disjoint loop data structure detected by our analysis. Eventually these data structures are ranked based on these measurements and are presented to the user to help manual tuning. We have performed a variety of studies on a set of 19 moderate/large-sized Java benchmarks. With the help of hoistability measurements, we found optimization opportunities in most of the programs that we inspected and achieved significant performance improvements in some of them (e.g., 82.1 % running time reduction). 1