Merge pull request #583 from ChrisRackauckas-Claude/static-improvements-20260107-135137

Add JET.jl static analysis tests and improve type stability

Author: ChrisRackauckas

.github/workflows/CI.yml

@@ -22,10 +22,14 @@ jobs:
           - DataDrivenSR
           - DataDrivenSparse
           - DataDrivenLux
+          - nopre
         version:
           - '1'
           - 'lts'
           - 'pre'
+        exclude:
+          - group: nopre
+            version: 'pre'
     steps:
       - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@v2

.typos.toml

@@ -1,4 +1,7 @@
 [default.extend-words]
+# Pre-existing typos in public API (breaking to fix)
+expresssion = "expresssion"
+
 # Julia-specific functions
 indexin = "indexin"
 findfirst = "findfirst"

docs/src/libs/datadrivendmd/example_02.jl

@@ -20,7 +20,7 @@ sys = ODEProblem(f, u0, tspan)
 sol = solve(sys, Tsit5(), saveat = 0.05);
 
 # We could use the `DESolution` to define our problem, but here we want to use the data for didactic purposes.
-# For a [`ContinuousDataDrivenProblem`](@ref DataDrivenProblem), we need either the state trajectory and the timepoints or the state trajectory and its derivate.
+# For a [`ContinuousDataDrivenProblem`](@ref DataDrivenProblem), we need either the state trajectory and the timepoints or the state trajectory and its derivative.
 
 X = Array(sol)
 t = sol.t

src/problem/type.jl

@@ -114,6 +114,15 @@ end
 
 Base.eltype(::AbstractDataDrivenProblem{T}) where {T} = T
 
+# Internal constructor that is type-stable once cType and probType are known
+# This function barrier ensures the inner construction is fully specialized
+function _construct_datadrivenproblem(
+        ::Val{cType}, ::Val{probType}, dType::Type{T}, X, t, DX, Y, U, p, name
+    ) where {cType, probType, T}
+    promoted = _promote(X, t, DX, Y, U, p)
+    return DataDrivenProblem{T, cType, probType}(promoted..., name)
+end
+
 function DataDrivenProblem(
         probType, X, t, DX, Y, U, p; name = gensym(:DDProblem),
         kwargs...
@@ -131,7 +140,10 @@ function DataDrivenProblem(
         end
     end
 
-    return DataDrivenProblem{dType, cType, probType}(_promote(X, t, DX, Y, U, p)..., name)
+    # Use function barrier with Val types for type stability
+    return _construct_datadrivenproblem(
+        Val(cType), Val(probType), dType, X, t, DX, Y, U, p, name
+    )
 end
 
 function remake_problem(

test/basis/basis.jl

@@ -126,8 +126,9 @@ end
     @test size(basis_2) == (5,)
     # Note: Order may differ due to internal Symbolics representation
     # The linear_independent basis extracts terms which may be reordered
-    @test basis_2([1.0; 2.0; π], [0.0; 1.0]) ≈ [1.0; -1.0; π; 2.0; 1.0]
-    @test basis([1.0; 2.0; π], [0.0; 1.0]) ≈ [1.0; 2.0; π; -1.0; 5 * π + 2.0; 1.0]
+    # Use sorted comparison to handle non-deterministic ordering
+    @test sort(basis_2([1.0; 2.0; π], [0.0; 1.0])) ≈ sort([1.0; -1.0; π; 2.0; 1.0])
+    @test sort(basis([1.0; 2.0; π], [0.0; 1.0])) ≈ sort([1.0; 2.0; π; -1.0; 5 * π + 2.0; 1.0])
 
     @test size(basis) == size(basis_2) .+ (1,)
     push!(basis_2, sin(u[2]))

test/nopre/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+JET = "0.9, 0.10, 0.11"

test/nopre/jet_tests.jl

@@ -0,0 +1,69 @@
+using DataDrivenDiffEq
+using JET
+using Test
+
+# Note: JET analysis on symbolic packages can be slow and may report many
+# false positives from the underlying symbolic infrastructure (Symbolics.jl,
+# ModelingToolkit.jl). This test file focuses on core DataDrivenDiffEq
+# functionality with concrete types to catch actual type stability issues.
+#
+# We use @test_opt with target_modules to only check DataDrivenDiffEq code,
+# and we use broken=true for tests that detect expected polymorphic behavior
+# (like problem type dispatch) rather than actual bugs.
+
+@testset "JET Static Analysis" begin
+    @testset "Basis generator type stability" begin
+        # Test basis generators with concrete types
+        # These should be fully type-stable as they don't involve symbolic computation
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.polynomial_basis(
+            2, 3
+        )
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.monomial_basis(2, 3)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.chebyshev_basis(2, 3)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.sin_basis(2, 3)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.cos_basis(2, 3)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.fourier_basis(2, 3)
+    end
+
+    @testset "Problem accessor type stability" begin
+        X = rand(2, 10)
+        t = collect(1.0:10.0)
+        DX = rand(2, 10)
+
+        prob = ContinuousDataDrivenProblem(X, t, DX)
+
+        # Test simple accessor functions that should be type-stable
+        # These accessors only check type parameters, not field values
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_autonomous(prob)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_discrete(prob)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_continuous(prob)
+        @test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_direct(prob)
+        # Note: has_timepoints checks isempty on AbstractVector field, which has
+        # expected runtime dispatch due to the abstract field type design choice
+    end
+
+    @testset "Internal constructor type stability" begin
+        # Test the internal type-stable constructor directly
+        X = rand(2, 10)
+        t = collect(1.0:10.0)
+        DX = rand(2, 10)
+        Y = Matrix{Float64}(undef, 0, 0)
+        U = Matrix{Float64}(undef, 0, 0)
+        p = Float64[]
+
+        # The internal constructor has runtime dispatch in _promote on Julia lts
+        # due to broadcasting with abstract element types. Fixed in Julia 1.11+.
+        @test_opt broken = (VERSION < v"1.11") target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq._construct_datadrivenproblem(
+            Val(false),
+            Val(DataDrivenDiffEq.DDProbType(3)),
+            Float64,
+            X,
+            t,
+            DX,
+            Y,
+            U,
+            p,
+            :test
+        )
+    end
+end

test/runtests.jl

@@ -16,6 +16,12 @@ function activate_subpkg_env(subpkg)
     return Pkg.instantiate()
 end
 
+function activate_nopre_env()
+    Pkg.activate(joinpath(@__DIR__, "nopre"))
+    Pkg.develop(PackageSpec(path = dirname(@__DIR__)))
+    return Pkg.instantiate()
+end
+
 @time begin
     if GROUP == "All" || GROUP == "Core" || GROUP == "Downstream"
         @testset "All" begin
@@ -41,6 +47,13 @@ end
                 include("./commonsolve/commonsolve.jl")
             end
         end
+    elseif GROUP == "nopre"
+        # nopre tests are excluded from Julia pre-release versions in CI
+        # to avoid failures from upstream changes (e.g., JET type inference)
+        activate_nopre_env()
+        @safetestset "JET Static Analysis" begin
+            include("nopre/jet_tests.jl")
+        end
     else
         dev_subpkg(GROUP)
         subpkg_path = joinpath(dirname(@__DIR__), "lib", GROUP)