Improve test coverage

test/test_constraint.jl

@@ -2171,4 +2171,51 @@ function test_constraint_by_name()
     return
 end
 
+function test_shadow_price_errors()
+    model = Model()
+    @variable(model, x[1:2])
+    c = @constraint(model, x in SOS1())
+    @test_throws(
+        ErrorException(
+            "The shadow price is not defined or not implemented for this type " *
+            "of constraint.",
+        ),
+        shadow_price(c),
+    )
+    err = ErrorException(
+        "The shadow price is not available because no dual result is " *
+        "available.",
+    )
+    model = Model()
+    @variable(model, x)
+    c = @constraint(model, x <= 1)
+    @test_throws err shadow_price(c)
+    c = @constraint(model, x >= 1)
+    @test_throws err shadow_price(c)
+    c = @constraint(model, x == 1)
+    @test_throws err shadow_price(c)
+
+    model = Model()
+    @variable(model, x >= 0)
+    set_optimizer(
+        model,
+        () -> MOI.Utilities.MockOptimizer(MOI.Utilities.Model{Float64}()),
+    )
+    optimize!(model)
+    mock = unsafe_backend(model)
+    MOI.set(mock, MOI.TerminationStatus(), MOI.OPTIMAL)
+    MOI.set(mock, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+    MOI.set(mock, MOI.DualStatus(), MOI.FEASIBLE_POINT)
+    F, S = MOI.VariableIndex, MOI.GreaterThan{Float64}
+    xi = only(MOI.get(mock, MOI.ListOfVariableIndices()))
+    MOI.set(mock, MOI.ConstraintDual(), MOI.ConstraintIndex{F,S}(xi.value), 1.0)
+    @test_throws(
+        ErrorException(
+            "The shadow price is not available because the objective sense $FEASIBILITY_SENSE is not minimization or maximization.",
+        ),
+        shadow_price(LowerBoundRef(x)),
+    )
+    return
+end
+
 end  # module

test/test_model.jl

@@ -1364,4 +1364,29 @@ function test_optimizer_index()
     return
 end
 
+function test_copy_nonlinear()
+    model = Model()
+    @variable(model, x)
+    @NLconstraint(model, x <= 1)
+    @test_throws(
+        ErrorException(
+            "copy is not supported yet for models with nonlinear constraints" *
+            " and/or nonlinear objective function",
+        ),
+        copy_model(model),
+    )
+    return
+end
+
+function test_deepcopy()
+    model = Model()
+    @test_throws(
+        ErrorException(
+            "`JuMP.Model` does not support `deepcopy` as the reference to the underlying solver cannot be deep copied, use `copy` instead.",
+        ),
+        deepcopy(model),
+    )
+    return
+end
+
 end  # module TestModels

test/test_nlp.jl

@@ -1683,4 +1683,46 @@ function test_get_node_type_comparison()
     return
 end
 
+function test_NL_dot()
+    model = Model()
+    @variable(model, x)
+    y = (; abc = x)
+    @NLexpression(model, ref, y.abc)
+    nlp = nonlinear_model(model)
+    @test MOI.Nonlinear.parse_expression(nlp, x) == nlp[index(ref)]
+    return
+end
+
+function test_NL_adjoint()
+    model = Model()
+    @variable(model, x)
+    @NLexpression(model, ref, x')
+    nlp = nonlinear_model(model)
+    @test MOI.Nonlinear.parse_expression(nlp, x) == nlp[index(ref)]
+    return
+end
+
+function test_NL_too_many_arguments_errors()
+    model = Model()
+    @test_throws_parsetime(
+        ErrorException(
+            "In `@NLexpression()`: To few arguments (0); must pass the model and nonlinear expression as arguments.",
+        ),
+        @NLexpression(),
+    )
+    @test_throws_parsetime(
+        ErrorException(
+            "In `@NLexpression(model, ex, 1, 2)`: To many arguments (4).",
+        ),
+        @NLexpression(model, ex, 1, 2),
+    )
+    @test_throws_parsetime(
+        ErrorException(
+            "In `@NLconstraint(model, ex, 1, 2)`: too many arguments.",
+        ),
+        @NLconstraint(model, ex, 1, 2),
+    )
+    return
+end
+
 end