Title
Domain Types: Selecting Abstractions Based on Variable Usage
Abstract
The success of software model checking depends on finding an appropriate abstraction of the subject program. The choice of the abstract domain and the analysis configuration is currently left to the user, who may not be familiar with the tradeoffs and performance details of the available abstract domains. We introduce the concept of domain types, which classify the program variables into types that are more fine-grained than standard declared types, such as int or long, in order to guide the selection of an appropriate abstract domain for a model checker. Our implementation determines the domain type for each variable in a pre-processing step, based on the variable usage in the program, and then assigns each variable to an abstract domain. The model-checking framework that we use supports to specify a separate analysis precision for each abstract domain, such that we can freely configure the analysis. We experimentally demonstrate a significant impact of the choice of the abstract domain per variable. We consider one explicit (hash tables for integer values) and one symbolic (binary decision diagrams) domain. The experiments are based on standard verification tasks that are taken from recent competitions on software verification. Each abstract domain has unique advantages in representing the state space of variables of a certain domain type. Our experiments show that software model checkers can be improved with a domain-type guided combination of abstract domains.
Year
Venue
Field
2013
CoRR
Domain analysis,Domain (software engineering),Model checking,Programming language,Domain engineering,Data domain,Computer science,Theoretical computer science,Business domain,Domain model,Software verification
DocType
Volume
Citations 
Journal
abs/1305.6640
1
PageRank 
References 
Authors
0.35
22
5
Name
Order
Citations
PageRank
Sven Apel13980184.13
Dirk Beyer21736100.85
Karlheinz Friedberger3173.33
Franco Raimondi483248.18
Alexander von Rhein531611.35