Synthesizing Abstract Transformers for Reduced-Product Domains
This program is tentative and subject to change.
Recently, we showed how to apply program-synthesis techniques to create abstract transformers in a user-provided domain-specific language (DSL) L (i.e., “L-transformers”). This algorithm does not scale when applied to reduced-product domains: synthesizing transformers for all of the component domains simultaneously blows up the search-space. Because reduced-product domains can significantly improve the preci- sion of abstract interpretation, in this paper, we propose an algorithm to synthesize reduced L-transformers ⟨f1♯R,f2♯R,…,fn♯R⟩ for a product domain A1 × A2 × ··· × An, using multiple DSLs: L = ⟨L1,L2,…,Ln⟩. Synthesis of reduced-product transformers is quite challenging: first, the synthesis task has to tackle an larger “feature set” as each component transformer now has access to the abstract inputs from all component domains in the product. Second, to ensure that the product transformer is maximally precise, the synthesis task needs to arrange for the component transformers to cooperate with each other. We implemented our algorithm in a tool, Amurth2, and used it to synthesize abstract transformers for two product domains—SAFE and JSAI—available within the SAFEstr framework for JavaScript program analysis. For four of the six operations supported by SAFEstr, Amurth2 synthesizes more precise abstract transformers than the manually written ones available in SAFEstr.
This program is tentative and subject to change.
Tue 22 OctDisplayed time zone: Pacific Time (US & Canada) change
09:00 - 10:30 | |||
09:00 60mKeynote | TBA SAS Mayur Naik University of Pennsylvania | ||
10:00 30mFull-paper | Synthesizing Abstract Transformers for Reduced-Product Domains SAS Pankaj Kumar Kalita IIT Kanpur, Thomas Reps University of Wisconsin-Madison, Subhajit Roy IIT Kanpur |