Partially disjunctive heap abstraction

Roman Manevich, Mooly Sagiv, Ganesan Ramalingam, John Field

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

27 Scopus citations

Abstract

One of the continuing challenges in abstract interpretation is the creation of abstractions that yield analyses that are both tractable and precise enough to prove interesting properties about real-world programs. One source of difficulty is the need to handle programs with different behaviors along different execution paths. Disjunctive (powerset) abstractions capture such distinctions in a natural way. However, in general, powerset abstractions increase space and time costs by an exponential factor. Thus, powerset abstractions are generally perceived as very costly. In this paper, we partially address this challenge by presenting and empirically evaluating a new heap abstraction. The new heap abstraction works by merging shape descriptors according to a partial isomorphism similarity criteria, resulting in a partially disjunctive abstraction. We implemented this abstraction in TVLA - a generic system for implementing program analyses. We conducted an empirical evaluation of the new abstraction and compared it with the powerset heap abstraction. The experiments show that analyses based on the partially disjunctive heap abstraction are as precise as the ones based on the powerset heap abstraction. In terms of performance, analyses based on the partially disjunctive heap abstraction are often superior to analyses based on the powerset heap abstraction. The empirical results show considerable speedups, up to 2 orders of magnitude, enabling previously non-terminating analyses, such as verification of the Deutsch-Schorr-Waite scanning algorithm, to terminate with no negative effect on the overall precision. Indeed, experience indicates that the partially disjunctive shape abstraction improves performance across all TVLA analyses uniformly, and in many cases is essential for making precise shape analysis feasible.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRoberto Giacobazzi
PublisherSpringer Verlag
Pages265-279
Number of pages15
ISBN (Print)9783540278641
DOIs
StatePublished - 1 Jan 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3148
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Dive into the research topics of 'Partially disjunctive heap abstraction'. Together they form a unique fingerprint.

Cite this