Demand-on-Demand Control-Flow Analysis
Year: 2026
Type: article
Abstract: Understanding program behaviors requires reasoning about control flow. Functional programs complicate this reasoning since call targets are computed in general. Control-flow analysis (CFA) can effectively reason about control flow (and much more) but is costly. Demand-driven CFA is less expensive but less versatile, and it is difficult to make it do what classical CFA does. This is unfortunate because many fundamental optimizations rely on control flow reasoning. In this paper, we present Demand-on-Demand Control-Flow Analysis (DoDCFA), a hybrid exhaustive–demand-driven CFA that recovers some of the versatility/applicability of classical CFA while improving on the speed of Demand CFA. The result is an analysis that can inexpensively and effectively analyze control flow and environment behavior sufficient to justify inlining.
DOI: 10.1145/3828687
Authors: Chahyun Kang, Kimball Germane
Institution: Brigham Young University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203628785