New Octagon Algorithm Eliminates Spurious Constraints in Static Analysis
A novel method reclassifies 6,930 invariants, reducing false precision in domain comparisons.
Numerical abstract domains are mathematical structures used in static analysis to infer program invariants—properties that always hold. Domains like Intervals, Zones, and Octagons vary in expressiveness: more expressive domains can yield more precise invariants but also risk introducing spurious constraints that distort comparisons. Previous work developed spurious constraint elimination for Zones; now, Ballou and Sherman extend this to Octagons with a novel algorithm that removes such constraints, enabling a fair minimal comparison of abstract states.
In their evaluation, the team analyzed 6,930 invariants from different abstract domains. The results show that minimal comparison reclassifies many invariants as equivalent across domains, significantly reducing the apparent precision advantage of Octagons. This means that for many practical static analysis tasks, the choice between Octagons and simpler domains may matter less than previously thought. The work has implications for software verification tool design, helping engineers select cost-effective domains without sacrificing accuracy.
- Novel spurious constraint elimination algorithm for Octagon abstract domains, building on prior Zone methods.
- Evaluated 6,930 invariants; minimal comparison found many were equivalent across domains.
- Reduces the perceived precision gap between Octagons and simpler domains, aiding domain selection.
Why It Matters
Enables more accurate static analysis tool selection, improving software verification efficiency and reliability.