From 83df5c2128d0d02535e1758f5371839ece74250b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Beno=C3=AEt=20Legat?= <benoit.legat@gmail.com>
Date: Tue, 18 Dec 2018 12:02:14 -0500
Subject: [PATCH] Update to MOI v0.8 and ModelMode

---
 REQUIRE                    |  2 +-
 docs/src/constraints.md    |  2 +-
 docs/src/quickstart.md     | 20 +++++-----
 docs/src/solvers.md        | 14 +++----
 docs/src/variables.md      |  4 +-
 src/JuMP.jl                | 44 ++++++++++-----------
 src/constraints.jl         | 14 +++----
 src/copy.jl                | 12 +++---
 src/macros.jl              | 18 ++++-----
 src/nlp.jl                 |  2 +-
 src/objective.jl           |  4 +-
 src/optimizer_interface.jl | 43 +++++++++++----------
 src/print.jl               | 12 +++---
 src/variables.jl           |  2 +-
 test/JuMPExtension.jl      |  3 +-
 test/constraint.jl         |  4 +-
 test/generate_and_solve.jl | 78 +++++++++++++++++++-------------------
 test/hygiene.jl            |  2 +-
 test/model.jl              |  2 +-
 test/nlp.jl                |  2 +-
 test/nlp_solver.jl         | 78 +++++++++++++++++++-------------------
 test/objective.jl          | 18 ++++-----
 test/print.jl              | 12 +++---
 23 files changed, 197 insertions(+), 195 deletions(-)

diff --git a/REQUIRE b/REQUIRE
index 7ab83d8feb6..198354b6ea6 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -1,5 +1,5 @@
 julia 0.7
-MathOptInterface 0.7 0.8
+MathOptInterface 0.8 0.9
 ForwardDiff 0.5 0.11
 Calculus
 DataStructures
diff --git a/docs/src/constraints.md b/docs/src/constraints.md
index 32364c4dd3f..cf793477eab 100644
--- a/docs/src/constraints.md
+++ b/docs/src/constraints.md
@@ -154,7 +154,7 @@ DocTestSetup = quote
     @objective(model, Max, -2x);
     JuMP.optimize!(model);
     mock = JuMP.backend(model).optimizer.model;
-    MOI.set(mock, MOI.DualStatus(), MOI.FeasiblePoint)
+    MOI.set(mock, MOI.DualStatus(), MOI.FEASIBLE_POINT)
     MOI.set(mock, MOI.ConstraintDual(), JuMP.optimizer_index(con), -2.0)
 end
 ```
diff --git a/docs/src/quickstart.md b/docs/src/quickstart.md
index 7c4986fdf88..4901c98edf5 100644
--- a/docs/src/quickstart.md
+++ b/docs/src/quickstart.md
@@ -30,8 +30,8 @@ julia> model = Model(with_optimizer(GLPK.Optimizer))
 A JuMP Model
 Feasibility problem with:
 Variables: 0
-Model mode: Automatic
-CachingOptimizer state: NoOptimizer
+Model mode: AUTOMATIC
+CachingOptimizer state: NO_OPTIMIZER
 Solver name: No optimizer attached.
 ```
 
@@ -99,9 +99,9 @@ DocTestSetup = quote
     # Now we load in the solution. Using a caching optimizer removes the need to
     # load a solver such as GLPK for building the documentation.
     mock = JuMP.backend(model).optimizer.model
-    MOI.set(mock, MOI.TerminationStatus(), MOI.Optimal)
-    MOI.set(mock, MOI.PrimalStatus(), MOI.FeasiblePoint)
-    MOI.set(mock, MOI.DualStatus(), MOI.FeasiblePoint)
+    MOI.set(mock, MOI.TerminationStatus(), MOI.OPTIMAL)
+    MOI.set(mock, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+    MOI.set(mock, MOI.DualStatus(), MOI.FEASIBLE_POINT)
     MOI.set(mock, MOI.ResultCount(), 1)
     MOI.set(mock, MOI.ObjectiveValue(), 10.6)
     MOI.set(mock, MOI.VariablePrimal(), JuMP.optimizer_index(x), 2.0)
@@ -120,9 +120,9 @@ to a setting such as a time limit. We can ask the solver why it stopped using
 the `JuMP.termination_status` function:
 ```jldoctest quickstart_example
 julia> JuMP.termination_status(model)
-Optimal::TerminationStatusCode = 1
+OPTIMAL::TerminationStatusCode = 1
 ```
-In this case, `GLPK` returned `Optimal`, this mean that it has found the optimal
+In this case, `GLPK` returned `OPTIMAL`, this mean that it has found the optimal
 solution.
 
 ```@meta
@@ -134,14 +134,14 @@ a primal-dual pair of feasible solutions with zero duality gap.
 We can verify the primal and dual status as follows to confirm this:
 ```jldoctest quickstart_example
 julia> JuMP.primal_status(model)
-FeasiblePoint::ResultStatusCode = 1
+FEASIBLE_POINT::ResultStatusCode = 1
 
 julia> JuMP.dual_status(model)
-FeasiblePoint::ResultStatusCode = 1
+FEASIBLE_POINT::ResultStatusCode = 1
 ```
 Note that the primal and dual status only inform that the primal and dual
 solutions are feasible and it is only because we verified that the termination
-status is `Optimal` that we can conclude that they form an optimal solution.
+status is `OPTIMAL` that we can conclude that they form an optimal solution.
 
 Finally, we can query the result of the optimization. First, we can query the
 objective value:
diff --git a/docs/src/solvers.md b/docs/src/solvers.md
index 0d815991b45..d99e0109d7f 100644
--- a/docs/src/solvers.md
+++ b/docs/src/solvers.md
@@ -32,18 +32,18 @@ that is completely transparent to JuMP. While the MOI API may seem very
 demanding, it allows MOI models to be a succession of lightweight MOI layers
 that fill the gap between JuMP requirements and the solver capabilities.
 
-JuMP models can be created in three different modes: Automatic, Manual and
-Direct.
+JuMP models can be created in three different modes: `AUTOMATIC`, `MANUAL` and
+`DIRECT`.
 
 ## Automatic and Manual modes
 
-In Automatic and Manual modes, two MOI layers are automatically applied to the
-optimizer:
+In `AUTOMATIC` and `MANUAL` modes, two MOI layers are automatically applied to
+the optimizer:
 
 * `CachingOptimizer`: maintains a cache of the model so that when the optimizer
   does not support an incremental change to the model, the optimizer's internal
   model can be discarded and restored from the cache just before optimization.
-  The `CachingOptimizer` has two different modes: Automatic and Manual
+  The `CachingOptimizer` has two different modes: `AUTOMATIC` and `MANUAL`
   corresponding to the two JuMP modes with the same names.
 * `LazyBridgeOptimizer` (this can be disabled using the `bridge_constraints`
   keyword argument to [`Model`](@ref) constructor): when a constraint added is
@@ -81,8 +81,8 @@ TODO: how to control the caching optimizer states
 
 ## Direct mode
 
-JuMP models can be created in Direct mode using the [`JuMP.direct_model`](@ref)
-function.
+JuMP models can be created in `DIRECT` mode using the
+[`JuMP.direct_model`](@ref) function.
 ```@docs
 JuMP.direct_model
 ```
diff --git a/docs/src/variables.md b/docs/src/variables.md
index 07aa964c89b..8418cc0240d 100644
--- a/docs/src/variables.md
+++ b/docs/src/variables.md
@@ -30,8 +30,8 @@ julia> model = Model()
 A JuMP Model
 Feasibility problem with:
 Variables: 0
-Model mode: Automatic
-CachingOptimizer state: NoOptimizer
+Model mode: AUTOMATIC
+CachingOptimizer state: NO_OPTIMIZER
 Solver name: No optimizer attached.
 
 julia> @variable(model, x[1:2])
diff --git a/src/JuMP.jl b/src/JuMP.jl
index 8d34163433e..676739f5535 100644
--- a/src/JuMP.jl
+++ b/src/JuMP.jl
@@ -129,11 +129,11 @@ end
 # Model
 
 # Model has three modes:
-# 1) Automatic: moi_backend field holds a CachingOptimizer in Automatic mode.
-# 2) Manual: moi_backend field holds a CachingOptimizer in Manual mode.
-# 3) Direct: moi_backend field holds an AbstractOptimizer. No extra copy of the model is stored. The moi_backend must support add_constraint etc.
+# 1) AUTOMATIC: moi_backend field holds a CachingOptimizer in AUTOMATIC mode.
+# 2) MANUAL: moi_backend field holds a CachingOptimizer in MANUAL mode.
+# 3) DIRECT: moi_backend field holds an AbstractOptimizer. No extra copy of the model is stored. The moi_backend must support add_constraint etc.
 # Methods to interact with the CachingOptimizer are defined in solverinterface.jl.
-@enum ModelMode Automatic Manual Direct
+@enum ModelMode AUTOMATIC MANUAL DIRECT
 
 abstract type AbstractModel end
 # All `AbstractModel`s must define methods for these functions:
@@ -152,8 +152,8 @@ mutable struct Model <: AbstractModel
     variable_to_fix::Dict{MOIVAR, MOIFIX}
     variable_to_integrality::Dict{MOIVAR, MOIINT}
     variable_to_zero_one::Dict{MOIVAR, MOIBIN}
-    # In Manual and Automatic modes, CachingOptimizer.
-    # In Direct mode, will hold an AbstractOptimizer.
+    # In MANUAL and AUTOMATIC modes, CachingOptimizer.
+    # In DIRECT mode, will hold an AbstractOptimizer.
     moi_backend::MOI.AbstractOptimizer
     # Hook into a solve call...function of the form f(m::Model; kwargs...),
     # where kwargs get passed along to subsequent solve calls.
@@ -171,7 +171,7 @@ mutable struct Model <: AbstractModel
 end
 
 """
-    Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
+    Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
             bridge_constraints::Bool=true)
 
 Return a new JuMP model without any optimizer; the model is stored the model in
@@ -182,7 +182,7 @@ optimizer are automatically bridged to equivalent supported constraints when
 an appropriate transformation is defined in the `MathOptInterface.Bridges`
 module or is defined in another module and is explicitely added.
 """
-function Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
+function Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
                  solver=nothing)
     if solver !== nothing
         error("The solver= keyword is no longer available in JuMP 0.19 and " *
@@ -197,7 +197,7 @@ end
 
 """
     Model(optimizer_factory::OptimizerFactory;
-          caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
+          caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
           bridge_constraints::Bool=true)
 
 Return a new JuMP model using the optimizer factory `optimizer_factory` to
@@ -272,19 +272,19 @@ MathOptInterface or solver-specific functionality.
 """
 backend(model::Model) = model.moi_backend
 
-moi_mode(model::MOI.ModelLike) = Direct
+moi_mode(model::MOI.ModelLike) = DIRECT
 function moi_mode(model::MOIU.CachingOptimizer)
-    if model.mode == MOIU.Automatic
-        return Automatic
+    if model.mode == MOIU.AUTOMATIC
+        return AUTOMATIC
     else
-        return Manual
+        return MANUAL
     end
 end
 
 """
     mode(model::Model)
 
-Return mode (Direct, Automatic, Manual) of model.
+Return mode (DIRECT, AUTOMATIC, MANUAL) of model.
 """
 mode(model::Model) = moi_mode(backend(model))
 # The type of backend(model) is unknown so we directly redirect to another
@@ -313,14 +313,14 @@ end
     solver_name(model::Model)
 
 If available, returns the `SolverName` property of the underlying optimizer.
-Returns `"No optimizer attached"` in `Automatic` or `Manual` modes when no
+Returns `"No optimizer attached"` in `AUTOMATIC` or `MANUAL` modes when no
 optimizer is attached. Returns
 "SolverName() attribute not implemented by the optimizer." if the attribute is
 not implemented.
 """
 function solver_name(model::Model)
-    if mode(model) != Direct &&
-        MOIU.state(backend(model)) == MOIU.NoOptimizer
+    if mode(model) != DIRECT &&
+        MOIU.state(backend(model)) == MOIU.NO_OPTIMIZER
         return "No optimizer attached."
     else
         return try_get_solver_name(backend(model))
@@ -451,19 +451,19 @@ Base.copy(v::AbstractArray{VariableRef}, new_model::Model) = (var -> VariableRef
 
 function optimizer_index(v::VariableRef)
     model = owner_model(v)
-    if mode(model) == Direct
+    if mode(model) == DIRECT
         return index(v)
     else
-        @assert backend(model).state == MOIU.AttachedOptimizer
+        @assert backend(model).state == MOIU.ATTACHED_OPTIMIZER
         return backend(model).model_to_optimizer_map[index(v)]
     end
 end
 
 function optimizer_index(cr::ConstraintRef{Model})
-    if mode(cr.model) == Direct
+    if mode(cr.model) == DIRECT
         return index(cr)
     else
-        @assert backend(cr.model).state == MOIU.AttachedOptimizer
+        @assert backend(cr.model).state == MOIU.ATTACHED_OPTIMIZER
         return backend(cr.model).model_to_optimizer_map[index(cr)]
     end
 end
@@ -478,7 +478,7 @@ return false.
 
 See also [`dual`](@ref) and [`shadow_price`](@ref).
 """
-has_duals(model::Model) = dual_status(model) != MOI.NoSolution
+has_duals(model::Model) = dual_status(model) != MOI.NO_SOLUTION
 
 """
     dual(cr::ConstraintRef)
diff --git a/src/constraints.jl b/src/constraints.jl
index f0148884b7f..f356b231830 100644
--- a/src/constraints.jl
+++ b/src/constraints.jl
@@ -218,7 +218,7 @@ end
 function moi_add_constraint(model::MOI.ModelLike, f::MOI.AbstractFunction,
                             s::MOI.AbstractSet)
     if !MOI.supports_constraint(model, typeof(f), typeof(s))
-        if moi_mode(model) == Direct
+        if moi_mode(model) == DIRECT
             bridge_message = "."
         elseif moi_bridge_constraints(model)
             bridge_message = " and there are no bridges that can reformulate it into supported constraints."
@@ -282,8 +282,8 @@ end
 
 The change in the objective from an infinitesimal relaxation of the constraint.
 This value is computed from [`dual`](@ref) and can be queried only when
-`has_duals` is `true` and the objective sense is `MinSense` or `MaxSense`
-(not `FeasibilitySense`). For linear constraints, the shadow prices differ at
+`has_duals` is `true` and the objective sense is `MIN_SENSE` or `MAX_SENSE`
+(not `FEASIBILITY_SENSE`). For linear constraints, the shadow prices differ at
 most in sign from the `dual` value depending on the objective sense.
 
 ## Notes
@@ -308,9 +308,9 @@ function shadow_price_less_than_(dual_value, sense::MOI.OptimizationSense)
     # shadow price is nonnegative (because relaxing a constraint can only
     # improve the objective). By MOI convention, a feasible dual on a LessThan
     # set is nonpositive, so we flip the sign when maximizing.
-    if sense == MOI.MaxSense
+    if sense == MOI.MAX_SENSE
         return -dual_value
-    elseif sense == MOI.MinSense
+    elseif sense == MOI.MIN_SENSE
         return dual_value
     else
         error("The shadow price is not available because the objective sense " *
@@ -322,9 +322,9 @@ end
 function shadow_price_greater_than_(dual_value, sense::MOI.OptimizationSense)
     # By MOI convention, a feasible dual on a GreaterThan set is nonnegative,
     # so we flip the sign when minimizing. (See comment in the method above).
-    if sense == MOI.MaxSense
+    if sense == MOI.MAX_SENSE
         return dual_value
-    elseif sense == MOI.MinSense
+    elseif sense == MOI.MIN_SENSE
         return -dual_value
     else
         error("The shadow price is not available because the objective sense " *
diff --git a/src/copy.jl b/src/copy.jl
index 5e76695fdc8..66214f52b75 100644
--- a/src/copy.jl
+++ b/src/copy.jl
@@ -68,7 +68,7 @@ the reference map.
 
 ## Note
 
-Model copy is not supported in Direct mode, i.e. when a model is constructed
+Model copy is not supported in `DIRECT` mode, i.e. when a model is constructed
 using the [`direct_model`](@ref) constructor instead of the [`Model`](@ref)
 constructor. Moreover, independently on whether an optimizer was provided at
 model construction, the new model will have no optimizer, i.e., an optimizer
@@ -90,10 +90,10 @@ cref_new = reference_map[cref]
 ```
 """
 function copy_model(model::Model)
-    if mode(model) == Direct
-        error("Cannot copy a model in Direct mode. Use the `Model` constructor",
-              " instead of the `direct_model` constructor to be able to copy",
-              " the constructed model.")
+    if mode(model) == DIRECT
+        error("Cannot copy a model in `DIRECT` mode. Use the `Model` ",
+              "constructor instead of the `direct_model` constructor to be ",
+              "able to copy the constructed model.")
     end
     caching_mode = backend(model).mode
     new_model = Model(caching_mode = caching_mode)
@@ -147,7 +147,7 @@ and its copy.
 
 ## Note
 
-Model copy is not supported in Direct mode, i.e. when a model is constructed
+Model copy is not supported in `DIRECT` mode, i.e. when a model is constructed
 using the [`direct_model`](@ref) constructor instead of the [`Model`](@ref)
 constructor. Moreover, independently on whether an optimizer was provided at
 model construction, the new model will have no optimizer, i.e., an optimizer
diff --git a/src/macros.jl b/src/macros.jl
index 549563e839c..7c410b87b2f 100644
--- a/src/macros.jl
+++ b/src/macros.jl
@@ -796,7 +796,7 @@ Constructs a vector of `QuadConstraint` objects. Similar to `@QuadConstraint`, e
 
 Return an expression whose value is an `MOI.OptimizationSense` corresponding
 to `sense`. Sense is either the symbol `:Min` or `:Max`, corresponding
-respectively to `MOI.MinSense` and `MOI.MaxSense` or it is another symbol,
+respectively to `MOI.MIN_SENSE` and `MOI.MAX_SENSE` or it is another symbol,
 which should be the name of a variable or expression whose value is an
 `MOI.OptimizationSense`.
 In the last case, the expression throws an error using the `_error`
@@ -804,9 +804,9 @@ function in case the value is not an `MOI.OptimizationSense`.
 """
 function moi_sense(_error::Function, sense)
     if sense == :Min
-        expr = MOI.MinSense
+        expr = MOI.MIN_SENSE
     elseif sense == :Max
-        expr = MOI.MaxSense
+        expr = MOI.MAX_SENSE
     else
         # Refers to a variable that holds the sense.
         # TODO: Better document this behavior
@@ -829,8 +829,8 @@ end
     @objective(model::Model, sense, func)
 
 Set the objective sense to `sense` and objective function to `func`. The
-objective sense can be either `Min`, `Max`, `MathOptInterface.MinSense`,
-`MathOptInterface.MaxSense` or `MathOptInterface.FeasibilitySense`; see
+objective sense can be either `Min`, `Max`, `MathOptInterface.MIN_SENSE`,
+`MathOptInterface.MAX_SENSE` or `MathOptInterface.FEASIBILITY_SENSE`; see
 [`MathOptInterface.ObjectiveSense`](http://www.juliaopt.org/MathOptInterface.jl/v0.6/apireference.html#MathOptInterface.ObjectiveSense).
 In order to set the sense programatically, i.e., when `sense` is a Julia
 variable whose value is the sense, one of the three
@@ -847,8 +847,8 @@ julia> model = Model()
 A JuMP Model
 Feasibility problem with:
 Variables: 0
-Model mode: Automatic
-CachingOptimizer state: NoOptimizer
+Model mode: AUTOMATIC
+CachingOptimizer state: NO_OPTIMIZER
 Solver name: No optimizer attached.
 
 julia> @variable(model, x)
@@ -867,8 +867,8 @@ julia> @objective(model, Max, 2x - 1)
 To set a quadratic objective and set the objective sense programatically, do
 as follows:
 ```jldoctest @objective
-julia> sense = JuMP.MOI.MinSense
-MinSense::OptimizationSense = 0
+julia> sense = JuMP.MOI.MIN_SENSE
+MIN_SENSE::OptimizationSense = 0
 
 julia> @objective(model, sense, x^2 - 2x + 1)
 x² - 2 x + 1
diff --git a/src/nlp.jl b/src/nlp.jl
index d1b25a325ae..41f2e71ffaf 100644
--- a/src/nlp.jl
+++ b/src/nlp.jl
@@ -1153,7 +1153,7 @@ function register(m::Model, s::Symbol, dimension::Integer, f::Function, ∇f::Fu
 end
 
 # TODO: Add a docstring.
-# Ex: set_NL_objective(model, MOI.MinSense, :($x + $y^2))
+# Ex: set_NL_objective(model, MOI.MIN_SENSE, :($x + $y^2))
 function set_NL_objective(model::Model, sense::MOI.OptimizationSense, x)
     return set_objective(model, sense, NonlinearExprData(model, x))
 end
diff --git a/src/objective.jl b/src/objective.jl
index 8496708f3e9..e124af3551f 100644
--- a/src/objective.jl
+++ b/src/objective.jl
@@ -103,8 +103,8 @@ julia> model = Model()
 A JuMP Model
 Feasibility problem with:
 Variables: 0
-Model mode: Automatic
-CachingOptimizer state: NoOptimizer
+Model mode: AUTOMATIC
+CachingOptimizer state: NO_OPTIMIZER
 Solver name: No optimizer attached.
 
 julia> @variable(model, x)
diff --git a/src/optimizer_interface.jl b/src/optimizer_interface.jl
index 44b030d7497..e030b666f99 100644
--- a/src/optimizer_interface.jl
+++ b/src/optimizer_interface.jl
@@ -4,32 +4,32 @@
 #  file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
 function error_if_direct_mode(model::Model, func::Symbol)
-    if mode(model) == Direct
-        error("The `$func` function is not supported in Direct mode.")
+    if mode(model) == DIRECT
+        error("The `$func` function is not supported in DIRECT mode.")
     end
 end
 
 # These methods directly map to CachingOptimizer methods.
 # They cannot be called in Direct mode.
-function MOIU.resetoptimizer!(model::Model, optimizer::MOI.AbstractOptimizer,
+function MOIU.reset_optimizer(model::Model, optimizer::MOI.AbstractOptimizer,
                               bridge_constraints::Bool=true)
-    error_if_direct_mode(model, :resetoptimizer!)
-    MOIU.resetoptimizer!(backend(model), optimizer)
+    error_if_direct_mode(model, :reset_optimizer)
+    MOIU.reset_optimizer(backend(model), optimizer)
 end
 
-function MOIU.resetoptimizer!(model::Model)
-    error_if_direct_mode(model, :resetoptimizer!)
-    MOIU.resetoptimizer!(backend(model))
+function MOIU.reset_optimizer(model::Model)
+    error_if_direct_mode(model, :reset_optimizer)
+    MOIU.reset_optimizer(backend(model))
 end
 
-function MOIU.dropoptimizer!(model::Model)
-    error_if_direct_mode(model, :dropoptimizer!)
-    MOIU.dropoptimizer!(backend(model))
+function MOIU.drop_optimizer(model::Model)
+    error_if_direct_mode(model, :drop_optimizer)
+    MOIU.drop_optimizer(backend(model))
 end
 
-function MOIU.attachoptimizer!(model::Model)
-    error_if_direct_mode(model, :attachoptimizer!)
-    MOIU.attachoptimizer!(backend(model))
+function MOIU.attach_optimizer(model::Model)
+    error_if_direct_mode(model, :attach_optimizer)
+    MOIU.attach_optimizer(backend(model))
 end
 
 function set_optimizer(model::Model, optimizer_factory::OptimizerFactory;
@@ -41,16 +41,17 @@ function set_optimizer(model::Model, optimizer_factory::OptimizerFactory;
         # If default_copy_to without names is supported, no need for a second
         # cache.
         if !MOIU.supports_default_copy_to(optimizer, false)
-            if mode(model) == Manual
+            if mode(model) == MANUAL
                 # TODO figure out what to do in manual mode with the two caches
-                error("Bridges in Manual mode with an optimizer not supporting `default_copy_to` is not supported yet")
+                error("Bridges in `MANUAL` mode with an optimizer not ",
+                      "supporting `default_copy_to` is not supported yet")
             end
             universal_fallback = MOIU.UniversalFallback(JuMPMOIModel{Float64}())
             optimizer = MOIU.CachingOptimizer(universal_fallback, optimizer)
         end
         optimizer = MOI.Bridges.fullbridgeoptimizer(optimizer, Float64)
     end
-    MOIU.resetoptimizer!(model, optimizer)
+    MOIU.reset_optimizer(model, optimizer)
 end
 
 """
@@ -92,15 +93,15 @@ function optimize!(model::Model,
     end
 
     if optimizer_factory !== nothing
-        if mode(model) == Direct
-            error("An optimizer factory cannot be provided at the `optimize` call in Direct mode.")
+        if mode(model) == DIRECT
+            error("An optimizer factory cannot be provided at the `optimize` call in DIRECT mode.")
         end
-        if MOIU.state(backend(model)) != MOIU.NoOptimizer
+        if MOIU.state(backend(model)) != MOIU.NO_OPTIMIZER
             error("An optimizer factory cannot both be provided in the `Model` constructor and at the `optimize` call.")
         end
         set_optimizer(model, optimizer_factory,
                       bridge_constraints=bridge_constraints)
-        MOIU.attachoptimizer!(model)
+        MOIU.attach_optimizer(model)
     end
 
     # If the user or an extension has provided an optimize hook, call
diff --git a/src/print.jl b/src/print.jl
index d9e705c0474..f1392a72507 100644
--- a/src/print.jl
+++ b/src/print.jl
@@ -150,9 +150,9 @@ function Base.show(io::IO, model::Model)
     plural(n) = (n==1 ? "" : "s")
     println(io, "A JuMP Model")
     sense = objective_sense(model)
-    if sense == MOI.MaxSense
+    if sense == MOI.MAX_SENSE
         print(io, "Maximization")
-    elseif sense == MOI.MinSense
+    elseif sense == MOI.MIN_SENSE
         print(io, "Minimization")
     else
         print(io, "Feasibility")
@@ -176,7 +176,7 @@ function Base.show(io::IO, model::Model)
     end
     model_mode = mode(model)
     println(io, "Model mode: ", model_mode)
-    if model_mode == Manual || model_mode == Automatic
+    if model_mode == MANUAL || model_mode == AUTOMATIC
         println(io, "CachingOptimizer state: ",
                 MOIU.state(backend(model)))
     end
@@ -202,14 +202,14 @@ function model_string(print_mode, model::Model)
     eol = ijl ? "\\\\\n" : "\n"
     sense = objective_sense(model)
     str = ""
-    if sense == MOI.MaxSense
+    if sense == MOI.MAX_SENSE
         str *= ijl ? "\\max" : "Max"
-    elseif sense == MOI.MinSense
+    elseif sense == MOI.MIN_SENSE
         str *= ijl ? "\\min" : "Min"
     else
         str *= ijl ? "\\text{feasibility}" : "Feasibility"
     end
-    if sense != MOI.FeasibilitySense
+    if sense != MOI.FEASIBILITY_SENSE
         if ijl
             str *= "\\quad"
         end
diff --git a/src/variables.jl b/src/variables.jl
index c6e3da11026..f2806e648c4 100644
--- a/src/variables.jl
+++ b/src/variables.jl
@@ -589,7 +589,7 @@ return false.
 
 See also [`value`](@ref).
 """
-has_values(model::Model) = primal_status(model) != MOI.NoSolution
+has_values(model::Model) = primal_status(model) != MOI.NO_SOLUTION
 
 @Base.deprecate setvalue(v::VariableRef, val::Number) set_start_value(v, val)
 
diff --git a/test/JuMPExtension.jl b/test/JuMPExtension.jl
index 7647e679360..a7ecd1d2e57 100644
--- a/test/JuMPExtension.jl
+++ b/test/JuMPExtension.jl
@@ -28,7 +28,8 @@ mutable struct MyModel <: JuMP.AbstractModel
             Dict{Int, String}(),                                 # Variables
             0, Dict{ConstraintIndex, JuMP.AbstractConstraint}(),
             Dict{ConstraintIndex, String}(),                     # Constraints
-            MOI.FeasibilitySense, zero(JuMP.GenericAffExpr{Float64, MyVariableRef}),
+            MOI.FEASIBILITY_SENSE,
+            zero(JuMP.GenericAffExpr{Float64, MyVariableRef}),
             Dict{Symbol, Any}())
     end
 end
diff --git a/test/constraint.jl b/test/constraint.jl
index e67d98fa27d..e4e14ff4e31 100644
--- a/test/constraint.jl
+++ b/test/constraint.jl
@@ -339,8 +339,8 @@ function test_shadow_price(model_string, constraint_dual, constraint_shadow)
                                          eval_objective_value=false,
                                          eval_variable_constraint_dual=false))
     mock_optimizer = JuMP.backend(model).optimizer.model
-    MOI.set(mock_optimizer, MOI.TerminationStatus(), MOI.Optimal)
-    MOI.set(mock_optimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+    MOI.set(mock_optimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
+    MOI.set(mock_optimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
     JuMP.optimize!(model)
 
     @testset "shadow price of $constraint_name" for constraint_name in keys(constraint_dual)
diff --git a/test/generate_and_solve.jl b/test/generate_and_solve.jl
index 23d42c703b5..196a9e8021a 100644
--- a/test/generate_and_solve.jl
+++ b/test/generate_and_solve.jl
@@ -44,11 +44,11 @@
                                          eval_objective_value=false))
 
         mockoptimizer = JuMP.backend(m).optimizer.model
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ObjectiveValue(), -1.0)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(y), 0.0)
         MOI.set(mockoptimizer, MOI.ConstraintDual(), JuMP.optimizer_index(c), -1.0)
@@ -58,15 +58,15 @@
         #@test JuMP.isattached(m)
         @test JuMP.has_values(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value(x) == 1.0
         @test JuMP.value(y) == 0.0
         @test JuMP.value(x + y) == 1.0
         @test JuMP.objective_value(m) == -1.0
 
-        @test JuMP.dual_status(m) == MOI.FeasiblePoint
+        @test JuMP.dual_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.dual(c) == -1
         @test JuMP.dual(JuMP.UpperBoundRef(x)) == 0.0
         @test JuMP.dual(JuMP.LowerBoundRef(y)) == 1.0
@@ -82,11 +82,11 @@
         @objective(m, Min, -x)
 
         c = @constraint(m, x + y <= 1)
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ObjectiveValue(), -1.0)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(y), 0.0)
         MOI.set(mockoptimizer, MOI.ConstraintDual(), JuMP.optimizer_index(c), -1.0)
@@ -98,15 +98,15 @@
         #@test JuMP.isattached(m)
         @test JuMP.has_values(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value(x) == 1.0
         @test JuMP.value(y) == 0.0
         @test JuMP.value(x + y) == 1.0
         @test JuMP.objective_value(m) == -1.0
 
-        @test JuMP.dual_status(m) == MOI.FeasiblePoint
+        @test JuMP.dual_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.dual(c) == -1
         @test JuMP.dual(JuMP.UpperBoundRef(x)) == 0.0
         @test JuMP.dual(JuMP.LowerBoundRef(y)) == 1.0
@@ -119,7 +119,7 @@
         m = Model(with_optimizer(MOIU.MockOptimizer,
                                  JuMP.JuMPMOIModel{Float64}(),
                                  eval_objective_value=false),
-                  caching_mode = MOIU.Automatic)
+                  caching_mode = MOIU.AUTOMATIC)
         @variable(m, x == 1.0, Int)
         @variable(m, y, Bin)
         @objective(m, Max, x)
@@ -140,24 +140,24 @@
         MOIU.loadfromstring!(model, modelstring)
         MOIU.test_models_equal(JuMP.backend(m).model_cache, model, ["x","y"], ["xfix", "xint", "ybin"])
 
-        MOIU.attachoptimizer!(m)
+        MOIU.attach_optimizer(m)
 
         mockoptimizer = JuMP.backend(m).optimizer.model
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ObjectiveValue(), 1.0)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(y), 0.0)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.NoSolution)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.NO_SOLUTION)
 
         JuMP.optimize!(m)
 
         #@test JuMP.isattached(m)
         @test JuMP.has_values(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value(x) == 1.0
         @test JuMP.value(y) == 0.0
@@ -193,11 +193,11 @@
                                          eval_objective_value=false))
 
         mockoptimizer = JuMP.backend(m).optimizer.model
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ObjectiveValue(), -1.0)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(y), 0.0)
         MOI.set(mockoptimizer, MOI.ConstraintDual(), JuMP.optimizer_index(c1), -1.0)
@@ -207,14 +207,14 @@
         #@test JuMP.isattached(m)
         @test JuMP.has_values(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value(x) == 1.0
         @test JuMP.value(y) == 0.0
         @test JuMP.objective_value(m) == -1.0
 
-        @test JuMP.dual_status(m) == MOI.FeasiblePoint
+        @test JuMP.dual_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.dual(c1) == -1.0
         @test JuMP.dual(c2) == 2.0
         @test JuMP.dual(c3) == 3.0
@@ -250,13 +250,13 @@
         mockoptimizer = MOIU.MockOptimizer(JuMP.JuMPMOIModel{Float64}(),
                                            eval_objective_value=false,
                                            eval_variable_constraint_dual=false)
-        MOIU.resetoptimizer!(m, mockoptimizer)
-        MOIU.attachoptimizer!(m)
+        MOIU.reset_optimizer(m, mockoptimizer)
+        MOIU.attach_optimizer(m)
 
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(y), 0.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(z), 0.0)
@@ -268,8 +268,8 @@
         #@test JuMP.isattached(m)
         @test JuMP.has_values(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value(x) == 1.0
         @test JuMP.value(y) == 0.0
@@ -311,13 +311,13 @@
         mockoptimizer = MOIU.MockOptimizer(JuMP.JuMPMOIModel{Float64}(),
                                            eval_objective_value=false,
                                            eval_variable_constraint_dual=false)
-        MOIU.resetoptimizer!(m, mockoptimizer)
-        MOIU.attachoptimizer!(m)
+        MOIU.reset_optimizer(m, mockoptimizer)
+        MOIU.attach_optimizer(m)
 
-        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.Optimal)
+        MOI.set(mockoptimizer, MOI.TerminationStatus(), MOI.OPTIMAL)
         MOI.set(mockoptimizer, MOI.ResultCount(), 1)
-        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FeasiblePoint)
-        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FeasiblePoint)
+        MOI.set(mockoptimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
+        MOI.set(mockoptimizer, MOI.DualStatus(), MOI.FEASIBLE_POINT)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x[1,1]), 1.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x[1,2]), 2.0)
         MOI.set(mockoptimizer, MOI.VariablePrimal(), JuMP.optimizer_index(x[2,2]), 4.0)
@@ -330,8 +330,8 @@
 
         JuMP.optimize!(m)
 
-        @test JuMP.termination_status(m) == MOI.Optimal
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.OPTIMAL
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.has_values(m)
         @test JuMP.value.(x) == [1.0 2.0; 2.0 4.0]
diff --git a/test/hygiene.jl b/test/hygiene.jl
index b524a8a7ead..a79c473dfea 100644
--- a/test/hygiene.jl
+++ b/test/hygiene.jl
@@ -12,7 +12,7 @@ using Test
 import JuMP
 
 model = JuMP.Model()
-sense = JuMP.MathOptInterface.MinSense
+sense = JuMP.MathOptInterface.MIN_SENSE
 JuMP.@variable(model, x >= 0)
 r = 3:5
 JuMP.@variable(model, y[i=r] <= i)
diff --git a/test/model.jl b/test/model.jl
index 07f5fc3105c..e0948b1c85f 100644
--- a/test/model.jl
+++ b/test/model.jl
@@ -158,7 +158,7 @@ function dummy_optimizer_hook(::JuMP.AbstractModel) end
 @testset "Model copy" begin
     for copy_model in (true, true)
         @testset "Using $(copy_model ? "JuMP.copy_model" : "Base.copy")" begin
-            for caching_mode in (MOIU.Automatic, MOIU.Manual)
+            for caching_mode in (MOIU.AUTOMATIC, MOIU.MANUAL)
                 @testset "In $caching_mode mode" begin
                     model = Model(caching_mode = caching_mode)
                     model.optimize_hook = dummy_optimizer_hook
diff --git a/test/nlp.jl b/test/nlp.jl
index a3211a26b86..4980465c1cf 100644
--- a/test/nlp.jl
+++ b/test/nlp.jl
@@ -463,7 +463,7 @@
         model = Model()
         @variable(model, x)
         @variable(model, y)
-        JuMP.set_NL_objective(model, MOI.MinSense, :($x^2 + $y^2))
+        JuMP.set_NL_objective(model, MOI.MIN_SENSE, :($x^2 + $y^2))
         JuMP.add_NL_constraint(model, :($x + $y <= 1))
         JuMP.add_NL_constraint(model, :($x + $y >= 1))
         JuMP.add_NL_constraint(model, :($x + $y == 1))
diff --git a/test/nlp_solver.jl b/test/nlp_solver.jl
index 8b880568fd8..e6e538095ea 100644
--- a/test/nlp_solver.jl
+++ b/test/nlp_solver.jl
@@ -47,8 +47,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value.(x) ≈ [1.000000, 4.742999, 3.821150, 1.379408] atol=1e-3
     end
@@ -65,7 +65,7 @@ const MOI = MathOptInterface
         m = Model(with_optimizer(Ipopt.Optimizer, print_level=0))
         initval = [1,5,5,1]
         @variable(m, 1 <= x[i=1:4] <= 5, start=initval[i])
-        JuMP.set_NL_objective(m, MOI.MinSense,
+        JuMP.set_NL_objective(m, MOI.MIN_SENSE,
                               :($(x[1]) * $(x[4]) *
                                 ($(x[1]) + $(x[2]) + $(x[3])) + $(x[3])))
         JuMP.add_NL_constraint(m,
@@ -78,8 +78,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value.(x) ≈ [1.000000, 4.742999, 3.821150, 1.379408] atol=1e-3
     end
@@ -126,8 +126,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.objective_value(m) ≈ 5326.851310161077 atol=1e-5
     end
@@ -144,9 +144,9 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        # Ipopt returns AlmostLocallySolved and NearlyFeasiblePoint on this instance.
-        # @test JuMP.termination_status(m) == MOI.LocallySolved
-        # @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        # Ipopt returns AlmostLOCALLY_SOLVED and NearlyFEASIBLE_POINT on this instance.
+        # @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        # @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.objective_value(m) ≈ -45.77846971 atol=1e-5
     end
@@ -167,8 +167,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.objective_value(m) ≈ -47.76109026 atol=1e-5
     end
@@ -188,8 +188,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.objective_value(m) ≈ -47.76109026 atol=1e-5
     end
@@ -223,8 +223,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.objective_value(m) ≈ -1768.80696 atol=1e-3
     end
@@ -269,8 +269,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         # This test occasionally fails, for unknown reasons.
         @test JuMP.objective_value(m) ≈ 97.588409 atol=1e-3
@@ -337,8 +337,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
         @test JuMP.value.(x[1:4]) ≈ [8.0, 49.0, 3.0, 1.0] atol=1e-4
         @test JuMP.objective_value(m) ≈ 664.82045 atol=1e-5
@@ -354,39 +354,39 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ u atol=1e-6
 
         @NLobjective(m, Min, x)
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ l atol=1e-6
     end
 
     @testset "Two-sided constraints (no macros)" begin
         m = Model(with_optimizer(Ipopt.Optimizer, print_level=0))
         @variable(m, x)
-        JuMP.set_NL_objective(m, MOI.MaxSense, x)
+        JuMP.set_NL_objective(m, MOI.MAX_SENSE, x)
         l = -1
         u = 1
         JuMP.add_NL_constraint(m, :($l <= $x <= $u))
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ u atol=1e-6
 
-        JuMP.set_NL_objective(m, MOI.MinSense, x)
+        JuMP.set_NL_objective(m, MOI.MIN_SENSE, x)
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ l atol=1e-6
     end
 
@@ -403,8 +403,8 @@ const MOI = MathOptInterface
 
         function test_result()
             @test JuMP.has_values(m)
-            @test JuMP.termination_status(m) == MOI.LocallySolved
-            @test JuMP.primal_status(m) == MOI.FeasiblePoint
+            @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+            @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
 
 
             @test JuMP.value(x) ≈ 0.9774436 atol=1e-6
@@ -414,7 +414,7 @@ const MOI = MathOptInterface
             @test JuMP.value(r[4]) ≈ 0.0 atol=1e-6
             @test JuMP.value(r[5]) ≈ 0.0 atol=1e-6
             @test JuMP.value(r[6]) ≈ 6.0 atol=1e-6
-            @test JuMP.dual_status(m) == MOI.FeasiblePoint
+            @test JuMP.dual_status(m) == MOI.FEASIBLE_POINT
             # Reduced costs
             @test JuMP.dual(JuMP.LowerBoundRef(x)) ≈ 0.0 atol=1e-6
             @test JuMP.dual(JuMP.UpperBoundRef(y)) ≈ 0.0 atol=1e-6
@@ -455,8 +455,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ -1-4/sqrt(3) atol=1e-6
         @test JuMP.value(x) + JuMP.value(y) ≈ -1/3 atol=1e-3
     end
@@ -470,8 +470,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ -1-4/sqrt(3) atol=1e-6
         @test JuMP.value(x) + JuMP.value(y) ≈ -1/3 atol=1e-3
     end
@@ -484,8 +484,8 @@ const MOI = MathOptInterface
         JuMP.optimize!(m)
 
         @test JuMP.has_values(m)
-        @test JuMP.termination_status(m) == MOI.LocallySolved
-        @test JuMP.primal_status(m) == MOI.FeasiblePoint
+        @test JuMP.termination_status(m) == MOI.LOCALLY_SOLVED
+        @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT
         @test JuMP.objective_value(m) ≈ sqrt(1/2) atol=1e-6
         @test JuMP.value.(x) ≈ [sqrt(1/2), 0] atol=1e-6
     end
diff --git a/test/objective.jl b/test/objective.jl
index 5960ab1a342..6ed6b7318e9 100644
--- a/test/objective.jl
+++ b/test/objective.jl
@@ -13,8 +13,8 @@ function objectives_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType:
 
     @testset "objective_sense set and get" begin
         model = ModelType()
-        JuMP.set_objective_sense(model, MOI.FeasibilitySense)
-        @test JuMP.objective_sense(model) == MOI.FeasibilitySense
+        JuMP.set_objective_sense(model, MOI.FEASIBILITY_SENSE)
+        @test JuMP.objective_sense(model) == MOI.FEASIBILITY_SENSE
     end
 
     @testset "SingleVariable objectives" begin
@@ -22,13 +22,13 @@ function objectives_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType:
         @variable(m, x)
 
         @objective(m, Min, x)
-        @test JuMP.objective_sense(m) == MOI.MinSense
+        @test JuMP.objective_sense(m) == MOI.MIN_SENSE
         @test JuMP.objective_function_type(m) == VariableRefType
         @test JuMP.objective_function(m) == x
         @test JuMP.objective_function(m, VariableRefType) == x
 
         @objective(m, Max, x)
-        @test JuMP.objective_sense(m) == MOI.MaxSense
+        @test JuMP.objective_sense(m) == MOI.MAX_SENSE
         @test JuMP.objective_function_type(m) == VariableRefType
         @test JuMP.objective_function(m) == x
         @test JuMP.objective_function(m, VariableRefType) == x
@@ -39,13 +39,13 @@ function objectives_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType:
         @variable(m, x)
 
         @objective(m, Min, 2x)
-        @test JuMP.objective_sense(m) == MOI.MinSense
+        @test JuMP.objective_sense(m) == MOI.MIN_SENSE
         @test JuMP.objective_function_type(m) == AffExprType
         @test JuMP.isequal_canonical(JuMP.objective_function(m), 2x)
         @test JuMP.isequal_canonical(JuMP.objective_function(m, AffExprType), 2x)
 
         @objective(m, Max, x + 3x + 1)
-        @test JuMP.objective_sense(m) == MOI.MaxSense
+        @test JuMP.objective_sense(m) == MOI.MAX_SENSE
         @test JuMP.objective_function_type(m) == AffExprType
         @test JuMP.isequal_canonical(JuMP.objective_function(m), 4x + 1)
         @test JuMP.isequal_canonical(JuMP.objective_function(m, AffExprType), 4x + 1)
@@ -56,7 +56,7 @@ function objectives_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType:
         @variable(m, x)
 
         @objective(m, Min, x^2 + 2x)
-        @test JuMP.objective_sense(m) == MOI.MinSense
+        @test JuMP.objective_sense(m) == MOI.MIN_SENSE
         @test JuMP.objective_function_type(m) == QuadExprType
         @test JuMP.isequal_canonical(JuMP.objective_function(m), x^2 + 2x)
         @test JuMP.isequal_canonical(JuMP.objective_function(m, QuadExprType), x^2 + 2x)
@@ -74,9 +74,9 @@ function objectives_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType:
         m = ModelType()
         @variable(m, x)
 
-        sense = MOI.MinSense
+        sense = MOI.MIN_SENSE
         @objective(m, sense, 2x)
-        @test JuMP.objective_sense(m) == MOI.MinSense
+        @test JuMP.objective_sense(m) == MOI.MIN_SENSE
         @test JuMP.isequal_canonical(JuMP.objective_function(m, AffExprType), 2x)
 
         sense = :Min
diff --git a/test/print.jl b/test/print.jl
index 841b4540ca9..fbd2123cdc6 100644
--- a/test/print.jl
+++ b/test/print.jl
@@ -413,8 +413,8 @@ function printing_test(ModelType::Type{<:JuMP.AbstractModel})
     `MathOptInterface.ScalarAffineFunction{Float64}`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
     `MathOptInterface.ScalarQuadraticFunction{Float64}`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
     `MathOptInterface.VectorAffineFunction{Float64}`-in-`MathOptInterface.SecondOrderCone`: 1 constraint
-    Model mode: Automatic
-    CachingOptimizer state: NoOptimizer
+    Model mode: AUTOMATIC
+    CachingOptimizer state: NO_OPTIMIZER
     Solver name: No optimizer attached.
     Names registered in the model: a, a1, b, b1, c, c1, fi, u, x, y, z""", repl=:show)
 
@@ -457,8 +457,8 @@ function printing_test(ModelType::Type{<:JuMP.AbstractModel})
     `MathOptInterface.SingleVariable`-in-`MathOptInterface.ZeroOne`: 1 constraint
     `MathOptInterface.SingleVariable`-in-`MathOptInterface.Integer`: 1 constraint
     `MathOptInterface.ScalarQuadraticFunction{Float64}`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
-    Model mode: Automatic
-    CachingOptimizer state: NoOptimizer
+    Model mode: AUTOMATIC
+    CachingOptimizer state: NO_OPTIMIZER
     Solver name: No optimizer attached.
     Names registered in the model: x, y""", repl=:show)
 
@@ -471,8 +471,8 @@ function printing_test(ModelType::Type{<:JuMP.AbstractModel})
     Feasibility problem with:
     Variable: 1
     `MathOptInterface.ScalarAffineFunction{Float64}`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
-    Model mode: Automatic
-    CachingOptimizer state: NoOptimizer
+    Model mode: AUTOMATIC
+    CachingOptimizer state: NO_OPTIMIZER
     Solver name: No optimizer attached.
     Names registered in the model: x""", repl=:show)
     end