Choosing A Static Analysis Tool
Created: 2026-06-10 10:00
#quicknote
Raw capability is rarely the deciding factor when selecting a static analysis tool. Three practical constraints prune the candidate list far more aggressively than rung depth does.
- Runtime footprint — the most capable engines (Joern, CodeQL) are JVM-based and memory-hungry: hundreds of MB of image, multiple GB of RAM at analysis time. In resource-constrained environments — serverless functions, small CI runners, edge deployments — "most capable" and "fits the budget" point in opposite directions. Lightweight binaries (OCaml, Go, Node) trade depth for deployability.
- Licensing — a hard gate for commercial software. MIT and Apache-2.0 are safe. GPL is viral and typically a non-starter for proprietary products. Weak copyleft (LGPL) needs legal review. Source-available licences (Elastic, Business Source) often restrict commercial or SaaS use and must not be mistaken for open source.
- Redundancy and engineering effort — if a capable commercial scanner is already in place, a second commercial tool must offer clear differential value, not overlap. Library tools (frameworks requiring months of engineering to productise) routinely lose to weaker turnkey tools despite superior raw capability. See Precision Concepts in Static Analysis for the turnkey vs. library distinction.
The decision is a trade-off between three axes: capability (rung + scope from The Code-Understanding Ladder and Flow Scope), fit (language coverage), and cost (footprint + licence + engineering effort). A pragmatic strategy under tight constraints is stacking cheap complementary layers — a navigation indexer, a focused taint engine, a lightweight rule scanner — rather than betting on one heavy CPG engine. See Static Analysis Tooling Landscape for how specific tools map to these buckets.
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#static_analysis #sast #tooling #security #decision