Abstract interpretation provides a systematic framework for static analysis, where widening operators are crucial for ensuring termination when analyzing programs over infinite-height lattices. Current abstract interpreters apply widening operations and fixpoint detection uniformly across all variables at the identified widening point (e.g., control-flow loop headers), leading to costly computations. Through our empirical study, we observe that infinite ascending chains typically originate from only a subset of variables involved in value-flow cycles, providing opportunities for selective widening and targeted fixpoint detection. This paper introduces an efficient approach to optimize abstract interpretation over non-relational domains through selective widening guided by value-flow analysis. We develop a modular and condensed value-flow graph (MVFG) that enables precise identification of variables requiring widening by detecting value-flow cycles across procedure boundaries. Our MVFG design incorporates efficient shortcut edges that summarize interprocedural value flows, achieving the precision of context-sensitive analysis but with linear complexity. By aligning value-flow cycles with the weak topological ordering (WTO) of the control-flow graph, we identify the minimal set of variables requiring widening operations, applying widening exclusively to variables that participate in value-flow back edges. Our evaluation on large-scale open-source projects shows that our selective widening approach reduces analysis time by up to 41.2% while maintaining identical precision. The method significantly reduces the number of widened variables by up to 99.5%, with greater benefits observed in larger codebases.

@article{paperbot2926,
  title = {Efficient Abstract Interpretation via Selective Widening},
  author = {Jiawei Wang and Xiao Cheng and Yulei Sui},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {9},
  number = {OOPSLA2},
  year = {2025},
  doi = {10.1145/3763083}
}