POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Systematic design of program analysis frameworks
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A Toolkit for Constructing Type- and Constraint-Based Program Analyses
TIC '98 Proceedings of the Second International Workshop on Types in Compilation
The Complexity of Set Constraints
CSL '93 Selected Papers from the 7th Workshop on Computer Science Logic
A framework for numeric analysis of array operations
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Heap analysis in the presence of collection libraries
PASTE '07 Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Lifting abstract interpreters to quantified logical domains
Proceedings of the 35th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Modular data structure verification
Modular data structure verification
Discovering properties about arrays in simple programs
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Sharing analysis of arrays, collections, and recursive structures
Proceedings of the 8th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Finding Loop Invariants for Programs over Arrays Using a Theorem Prover
FASE '09 Proceedings of the 12th International Conference on Fundamental Approaches to Software Engineering: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
Abstraction Refinement for Quantified Array Assertions
SAS '09 Proceedings of the 16th International Symposium on Static Analysis
Array abstractions from proofs
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Quantified invariant generation using an interpolating saturation prover
TACAS'08/ETAPS'08 Proceedings of the Theory and practice of software, 14th international conference on Tools and algorithms for the construction and analysis of systems
Symbolic heap abstraction with demand-driven axiomatization of memory invariants
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
A parametric segmentation functor for fully automatic and scalable array content analysis
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Precise reasoning for programs using containers
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
FIXBAG: a fixpoint calculator for quantified bag constraints
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Hob: a tool for verifying data structure consistency
CC'05 Proceedings of the 14th international conference on Compiler Construction
What's decidable about arrays?
VMCAI'06 Proceedings of the 7th international conference on Verification, Model Checking, and Abstract Interpretation
Fluid updates: beyond strong vs. weak updates
ESOP'10 Proceedings of the 19th European conference on Programming Languages and Systems
Abstract domains for automated reasoning about list-manipulating programs with infinite data
VMCAI'12 Proceedings of the 13th international conference on Verification, Model Checking, and Abstract Interpretation
| Hi-index | 0.00 |
Programs written in modern languages perform intricate manipulations of containers such as arrays, lists, dictionaries, and sets. We present an abstract interpretation-based framework for automatically inferring relations between the set of values stored in these containers. Relations include inclusion relations over unions and intersections, as well as quantified relationships with scalar variables. We develop an abstract domain constructor that builds a container domain out of a Quantified Union-Intersection Constraint (QUIC) graph parameterized by an arbitrary base domain. We instantiate our domain with a polyhedral base domain and evaluate it on programs extracted from the Python test suite. Over traditional, non-relational domains, we find significant precision improvements with minimal performance cost.