Pdf Incrementalizing Lattice Based Program Analyses In Datalog To this end, we present a novel algorithm called dredl that supports incremental maintenance of recursive lattice value aggregation in datalog. the key insight of dredl is to dynamically recognize increasing replacements of old lattice values by new ones, which allows us to avoid the expensive deletion of the old value. Inca is a framework for specifying and incrementally executing program analyses that perform recursive lattice based aggregations. inca provides a dsl for the specification of lattices and program analyses.
Incrementalizing Lattice Based Program Analyses In Datalog R Since we want to incrementalize a wide range of whole program analyses, our new incremental datalog solver must support recursive lattice based aggregation. for example, consider a lattice bot ⊑ o(obj) ⊑ c(cls), where o and c are singleton constructors for objects and class types, respectively. To this end, we present a novel algorithm called dredl that supports incremental maintenance of recursive lattice value aggregation in datalog. the key insight of dredl is to dynamically. Our extension is based on a novel algorithm that enables the incremental maintenance of recursive lattice value aggregation, which occurs when analyzing code with cyclic control flow by fixpoint iteration. To demonstrate our approach, we realized strong update points to analysis and string analyses for java in incal and present performance measurements that demonstrate incremental analysis updates within milliseconds.
On Abstraction Refinement For Program Analyses In Datalog Our extension is based on a novel algorithm that enables the incremental maintenance of recursive lattice value aggregation, which occurs when analyzing code with cyclic control flow by fixpoint iteration. To demonstrate our approach, we realized strong update points to analysis and string analyses for java in incal and present performance measurements that demonstrate incremental analysis updates within milliseconds. Our datalog solver uses a non standard aggregation semantics which allows us to loosen monotonicity requirements on analyses and to improve the performance of lattice aggregators considerably. To solve this problem, we take our existing inca incremental program analysis framework that supports relational analyses, and we extend it with lattice based computations. In this paper, we study how to use incrementality to speed up program analyses without a ecting precision. compared to a from scratch re computation, an incremental analysis re uses previous results and updates them based on the changes in the subject program. Incrementalizing lattice based program analyses in datalogpdf: full text bibtex.
Comments are closed.