Recognition of Logically Related Regions Based Heap Abstraction

TL;DR

This paper introduces new algorithms for heap abstraction that identify related regions in memory, improving static analysis and enabling optimizations like garbage collection and object pooling.

Contribution

It proposes novel algorithms for heap abstraction of recursive data structures, enhancing static analysis efficiency and supporting program optimization techniques.

Findings

Algorithms for heap component abstraction are proven correct and terminating.

The approach models heap structures like lists, trees, cycles, and DAGs.

Heap abstraction improves static analysis convergence and optimization potential.

Abstract

This paper presents a novel set of algorithms for heap abstraction, identifying logically related regions of the heap. The targeted regions include objects that are part of the same component structure (recursive data structure). The result of the technique outlined in this paper has the form of a compact normal form (an abstract model) that boosts the efficiency of the static analysis via speeding its convergence. The result of heap abstraction, together with some properties of data structures, can be used to enable program optimizations like static deallocation, pool allocation, region-based garbage collection, and object co-location. More precisely, this paper proposes algorithms for abstracting heap components with the layout of a singly linked list, a binary tree, a cycle, and a directed acyclic graph. The termination and correctness of these algorithms are studied in the paper. Towards presenting the algorithms the paper also presents concrete and abstract models for heap representations.