Researchers bypass control flow graphs to speed up pointer analysis

Pointer analysis—a core technique for understanding how programs manipulate memory—faces efficiency challenges when designed to account for program control flow. Researchers have now proposed a simpler structural approach that maintains precision while dramatically cutting computation time and memory usage.

The Problem with Flow-Sensitive Analysis#

Traditional flow-sensitive pointer analysis relies on control flow graphs (CFGs) to track how pointers behave along different execution paths. While this approach yields high-precision results, the graph structures become unwieldy at scale, making analysis slow and resource-intensive. Programs with complex branching and loops create graphs that are expensive to traverse and solve, hobbling the technique on large codebases.

Flow-sensitive analysis is widely used in alias analysis, taint analysis, program understanding, and compiler optimization—making efficiency gains valuable across the board.

The New Approach: Flow-Sensitive Constraint Graphs#

The researchers introduced CG-FSPTA, which replaces control flow graphs with flow-sensitive constraint graphs (FSConsG). Constraint graphs are simpler structures traditionally used in flow-insensitive analysis; the innovation here is adapting them to preserve flow sensitivity where it matters—tracking how variable definitions and uses change across the program.

By decoupling flow sensitivity from the graph topology itself, the method leverages existing graph optimization and dynamic-solving techniques developed for simpler constraint systems, avoiding the overhead of traversing complex control flow structures.

Results and Impact#

Benchmark testing showed striking improvements: CG-FSPTA achieved 33% average memory reduction and 7.27x speedup compared to state-of-the-art flow-sensitive methods, all while maintaining the same analysis precision. These gains suggest the technique scales well to large software systems that current methods struggle with.

The work appears as a research paper submitted to arXiv (cs.SE) and demonstrates that rethinking the underlying graph representation—rather than refining existing structures—can unlock significant practical efficiency.