Compositional recurrence analysis revisited

23Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Compositional recurrence analysis (CRA) is a static-analysis method based on a combination of symbolic analysis and abstract interpretation. This paper addresses the problem of creating a context-sensitive interprocedural version of CRA that handles recursive procedures. The problem is non-trivial because there is an "impedance mismatch" between CRA, which relies on analysis techniques based on regular languages (i.e., Tarjan's path-expression method), and the context-free-language underpinnings of context-sensitive analysis. We show how to address this impedance mismatch by augmenting the CRA abstract domain with additional operations. We call the resulting algorithm Interprocedural CRA (ICRA). Our experiments with ICRA show that it has broad overall strength compared with several state-of-the-art software model checkers.

Author supplied keywords

References Powered by Scopus

Abstract interpretation: "A" unified lattice model for static analysis of programs by construction or approximation of fixpoints

4534Citations
N/AReaders
Get full text

Automatic discovery of linear restraints among variables of a program

1187Citations
N/AReaders
Get full text

Program analysis via graph reachability

236Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Learning nonlinear loop invariants with gated continuous logic networks

33Citations
N/AReaders
Get full text

Polynomial invariant generation for non-deterministic recursive programs

27Citations
N/AReaders
Get full text

Proving non-termination by program reversal

14Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Kincaid, Z., Breck, J., Boroujeni, A. F., & Reps, T. (2017). Compositional recurrence analysis revisited. ACM SIGPLAN Notices, 52(6), 248–262. https://doi.org/10.1145/3062341.3062373

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 12

100%

Readers' Discipline

Tooltip

Computer Science 15

88%

Business, Management and Accounting 1

6%

Psychology 1

6%

Save time finding and organizing research with Mendeley

Sign up for free