🤖 AI Summary
Traditional static analysis struggles to balance precision, reliability, and automation, limiting its practical applicability. This work proposes a novel parameterized static analysis approach that introduces user-provided local assumptions at selected program locations and incorporates them via a nondeterministic semantics, thereby constructing a mapping from sets of assumptions to analysis results. This formulation enables optimization-based search over large assumption spaces, overcoming conventional precision bottlenecks. The method’s effectiveness is demonstrated through experiments in two representative scenarios, significantly enhancing the adaptability and flexibility of static analysis in real-world applications.
📝 Abstract
Static analyses overwhelmingly trade precision for soundness and automation. For this reason, their use-cases are restricted to situations where imprecision isn't prohibitive. In this paper, we propose and specify a static analysis that accepts user-supplied program assumptions that are local to program locations. Such assumptions can be used to counteract imprecision in static analyses, enabling their use in a much wider variety of applications. These assumptions are taken by the analyzer non-deterministically, resulting in a function from sets of accepted assumptions to the resulting analysis under those assumptions. We also demonstrate the utility of such a function in two ways, both of which showcase how it can enable optimization over a search space of assumptions that is otherwise infeasible without the specified analysis.