Abstracting Runtime Heaps for Program Understanding

TL;DR

This paper introduces a technique for abstracting runtime heaps to aid program understanding, enabling high-level analysis of heap structures and relationships, demonstrated through a memory profiler for detecting memory bloat.

Contribution

The paper presents a novel abstract heap model and algorithms for transforming, merging, and comparing heap data to improve runtime heap analysis and program comprehension.

Findings

Effective abstraction of runtime heaps emphasizing shape and size.

Algorithms for heap transformation and comparison are computationally tractable.

Built a memory profiler that detects and corrects memory bloat in real programs.

Abstract

Modern programming environments provide extensive support for inspecting, analyzing, and testing programs based on the algorithmic structure of a program. Unfortunately, support for inspecting and understanding runtime data structures during execution is typically much more limited. This paper provides a general purpose technique for abstracting and summarizing entire runtime heaps. We describe the abstract heap model and the associated algorithms for transforming a concrete heap dump into the corresponding abstract model as well as algorithms for merging, comparing, and computing changes between abstract models. The abstract model is designed to emphasize high-level concepts about heap-based data structures, such as shape and size, as well as relationships between heap structures, such as sharing and connectivity. We demonstrate the utility and computational tractability of the abstract heap model by building a memory profiler. We then use this tool to check for, pinpoint, and correct sources of memory bloat from a suite of programs from DaCapo.