Fully Automated Shape Analysis Based on Forest Automata

TL;DR

This paper introduces a fully automated shape analysis method using forest automata that automatically learns complex heap graph patterns, enabling efficient analysis of advanced data structures like skip lists.

Contribution

It presents a novel automatic learning technique for forest automata boxes and improves automata abstraction, enhancing analysis of complex heap structures.

Findings

Handles complex data structures like skip lists

Performance comparable to specialized separation logic tools

Automates the learning of repetitive graph patterns

Abstract

Forest automata (FA) have recently been proposed as a tool for shape analysis of complex heap structures. FA encode sets of tree decompositions of heap graphs in the form of tuples of tree automata. In order to allow for representing complex heap graphs, the notion of FA allowed one to provide user-defined FA (called boxes) that encode repetitive graph patterns of shape graphs to be used as alphabet symbols of other, higher-level FA. In this paper, we propose a novel technique of automatically learning the FA to be used as boxes that avoids the need of providing them manually. Further, we propose a significant improvement of the automata abstraction used in the analysis. The result is an efficient, fully-automated analysis that can handle even as complex data structures as skip lists, with the performance comparable to state-of-the-art fully-automated tools based on separation logic, which, however, specialise in dealing with linked lists only.