constraint

Concept

A constraint, in the constrained-random verification paradigm, is a user-defined callable (typically a Python function) registered on a randomized object to restrict the legal combinations of its random variables. Constraints consume the current values of the variables listed in their dependency tuple and return a boolean, allowing the random solver to reject or accept a proposed randomization.

First seen 6/22/2026

Last seen 6/22/2026

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Definition

In constrained-random test generation, a constraint is a predicate that is attached to a randomized object so that the generator only produces values satisfying the predicate. Each constraint is supplied together with a tuple naming the random variables whose values are passed to it as arguments.

Mechanism in `constrainedrandom`

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ldInstr ← uses 100% 1e

ldInstr adds constraints to enforce writeback and word-alignment rules using add_constraint.

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[1] In the `constrainedrandom` library, a constraint is registered via `add_constraint(fn, (var1, var2, ...))`; the function receives the named variables' current values and returns a boolean used to gate randomization. Examples — constrainedrandom 1.0.0 documentation

[2] The `ldInstr` example defines a `wb_dst_src` constraint that forbids `dst0 == src0` when writeback is enabled, and a `sum_src0_imm0` constraint enforcing word alignment and no 32-bit overflow on `src0_value + imm0`. Examples — constrainedrandom 1.0.0 documentation

[3] In constraint logic programming, constraints generalize equality over Herbrand terms to decidable constraint theories, subsuming Prolog and enabling declarative combinatorial problem solving. A Constraint-based Mathematical Modeling Library in Prolog with Answer Constraint Semantics

[4] Constraint-based reachability interprets imperative constructs (conditionals, loops, arrays, memory) as constraints over a computational domain and reuses classical filtering consistencies from Constraint Programming to analyze infinite-state systems. Constraint-based reachability