Replace remaining @test_broken with @test ... broken = ...

Convert the rest of the `@test_broken` calls in `test_basics.jl` and
`test_map.jl` to the `broken = ...` keyword form, fold the surviving
`if arrayt === Array; ...; else; @test_broken ...; end` pair in
`test_map.jl` into a single `@test ... broken = arrayt ≢ Array`, and
drop the now-unused `@test_broken` import from `test_genericblockindex.jl`
and `test_tensoralgebraext.jl`.

Also gate the LQ regression broken marker in `test_abstract_blocktype.jl`
on `pkgversion(MatrixAlgebraKit) < v"0.6.7"` so the marker auto-disables
once the upstream LQ gauge-fixing fix lands in MatrixAlgebraKit 0.6.7
(QuantumKitHub/MatrixAlgebraKit.jl#219).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: mtfishman

test/test_abstract_blocktype.jl

@@ -3,9 +3,9 @@ using BlockArrays: Block
 using BlockSparseArrays: BlockSparseMatrix, blockstoredlength
 using JLArrays: JLArray
 using LinearAlgebra: hermitianpart, norm
-using MatrixAlgebraKit: eig_full, eig_trunc, eig_vals, eigh_full, eigh_trunc, eigh_vals,
-    isisometric, left_orth, left_polar, lq_compact, lq_full, qr_compact, qr_full,
-    right_orth, right_polar, svd_compact, svd_full, svd_trunc
+using MatrixAlgebraKit: MatrixAlgebraKit, eig_full, eig_trunc, eig_vals, eigh_full,
+    eigh_trunc, eigh_vals, isisometric, left_orth, left_polar, lq_compact, lq_full,
+    qr_compact, qr_full, right_orth, right_polar, svd_compact, svd_full, svd_trunc
 using SparseArraysBase: storedlength
 using StableRNGs: StableRNG
 using Test: @test, @testset
@@ -97,9 +97,12 @@ arrayts = (Array, JLArray)
         c, u = f(a)
         # `right_orth`, `lq_compact`, `lq_full` on JLArray-backed
         # `BlockSparseMatrix{T, AbstractMatrix{T}}` produce a `c * u` that does not
-        # reproduce `a` (regression in MatrixAlgebraKit 0.6.6, tracked at
-        # https://github.com/QuantumKitHub/MatrixAlgebraKit.jl/issues/218).
-        @test c * u ≈ a broken = (arrayt ≢ Array && f ∈ (right_orth, lq_compact, lq_full))
+        # reproduce `a` (regression in MatrixAlgebraKit 0.6.6; fix tracked at
+        # https://github.com/QuantumKitHub/MatrixAlgebraKit.jl/pull/219).
+        @test c * u ≈ a broken = (
+            arrayt ≢ Array && f ∈ (right_orth, lq_compact, lq_full) &&
+                pkgversion(MatrixAlgebraKit) < v"0.6.7"
+        )
         # TODO: Fix comparison with UniformScaling on GPU.
         @test isisometric(u; side = :right) broken = arrayt ≢ Array
     end

test/test_basics.jl

@@ -11,7 +11,7 @@ using JLArrays: JLArray, JLMatrix
 using LinearAlgebra: Adjoint, Transpose, dot, norm, tr
 using SparseArraysBase:
     SparseArrayDOK, SparseMatrixDOK, SparseVectorDOK, isstored, storedlength
-using Test: @inferred, @test, @test_broken, @test_throws, @testset
+using Test: @inferred, @test, @test_throws, @testset
 using TestExtras: @constinferred
 using TypeParameterAccessors: TypeParameterAccessors, Position
 include("TestBlockSparseArraysUtils.jl")
@@ -28,18 +28,18 @@ arrayts = (Array, JLArray)
         a = dev(BlockSparseArray{elt}(undef, [2, 3], [2, 3]))
         a[Block(1, 1)] = dev(randn(elt, 2, 2))
         a[Block(2, 2)] = dev(randn(elt, 3, 3))
-        @test_broken a[:, 4]
+        @test a[:, 4] broken = true
 
         # TODO: Fix this and turn it into a proper test.
         a = dev(BlockSparseArray{elt}(undef, [2, 3], [2, 3]))
         a[Block(1, 1)] = dev(randn(elt, 2, 2))
         a[Block(2, 2)] = dev(randn(elt, 3, 3))
         @allowscalar @test a[2:4, 4] == Array(a)[2:4, 4]
-        @test_broken a[4, 2:4]
+        @test a[4, 2:4] broken = true
 
         @test a[Block(1), :] isa BlockSparseArray{elt}
         @test adjoint(a) isa Adjoint{elt, <:BlockSparseArray}
-        @test_broken adjoint(a)[Block(1), :] isa Adjoint{elt, <:BlockSparseArray}
+        @test adjoint(a)[Block(1), :] isa Adjoint{elt, <:BlockSparseArray} broken = true
         # could also be directly a BlockSparseArray
     end
     @testset "Constructors" begin
@@ -153,10 +153,10 @@ arrayts = (Array, JLArray)
         a = arrayt(randn(elt, 2, 2))
         @test (@constinferred blockstype(a)) <: BlockArrays.BlockView{elt, 2}
         # TODO: This is difficult to determine just from type information.
-        @test_broken blockstype(typeof(a)) <: BlockArrays.BlockView{elt, 2}
+        @test blockstype(typeof(a)) <: BlockArrays.BlockView{elt, 2} broken = true
         @test (@constinferred blocktype(a)) <: arrayt{elt, 2}
         # TODO: This is difficult to determine just from type information.
-        @test_broken blocktype(typeof(a)) <: SubArray{elt, 2, arrayt{elt, 2}}
+        @test blocktype(typeof(a)) <: SubArray{elt, 2, arrayt{elt, 2}} broken = true
 
         a = BlockSparseMatrix{elt, arrayt{elt, 2}}(undef, [1, 1], [1, 1])
         @test (@constinferred blockstype(a)) <: SparseMatrixDOK{arrayt{elt, 2}}
@@ -173,9 +173,9 @@ arrayts = (Array, JLArray)
         a = BlockedArray(arrayt(randn(elt, 2, 2)), [1, 1], [1, 1])
         @test (@constinferred blockstype(a)) <: BlockArrays.BlocksView{elt, 2}
         # TODO: This is difficult to determine just from type information.
-        @test_broken blockstype(typeof(a)) <: BlockArrays.BlocksView{elt, 2}
+        @test blockstype(typeof(a)) <: BlockArrays.BlocksView{elt, 2} broken = true
         @test blocktype(a) <: AbstractMatrix{elt}
-        @test_broken blocktype(typeof(a)) <: AbstractMatrix{elt}
+        @test blocktype(typeof(a)) <: AbstractMatrix{elt} broken = true
 
         # sparsemortar
         for ax in (

test/test_genericblockindex.jl

@@ -3,7 +3,7 @@ using BlockArrays: Block, BlockIndex, BlockSlice, BlockedArray, BlockedVector, b
 using BlockSparseArrays: BlockSparseArrays, BlockIndexVector, BlockIndices,
     GenericBlockIndex, blockedunitrange_getindices, blocksparsezeros, to_block,
     to_block_indices, to_blockindexrange
-using Test: @test, @test_broken, @testset
+using Test: @test, @testset
 
 # blockrange
 # checkindex

test/test_map.jl

@@ -7,7 +7,7 @@ using GPUArraysCore: @allowscalar
 using JLArrays: JLArray
 using SparseArraysBase: storedlength
 using StableRNGs: StableRNG
-using Test: @test, @test_broken, @test_throws, @testset
+using Test: @test, @test_throws, @testset
 
 elts = (Float32, Float64, ComplexF32)
 arrayts = (Array, JLArray)
@@ -407,12 +407,8 @@ arrayts = (Array, JLArray)
     a[Block(1, 1)] = dev(randn(elt, 2, 2))
     a[Block(2, 2)] = dev(randn(elt, 3, 3))
     I = (:, [2, 4])
-    if arrayt === Array
-        @test Array(a[I...]) == Array(a)[I...]
-    else
-        # TODO: Broken on GPU, fix this.
-        @test_broken a[I...]
-    end
+    # TODO: Broken on GPU, fix this.
+    @test Array(a[I...]) == Array(a)[I...] broken = arrayt ≢ Array
 
     a = BlockSparseArray{elt}(undef, [2, 3], [2, 3])
     @views for b in [Block(1, 1), Block(2, 2)]
@@ -718,5 +714,5 @@ arrayts = (Array, JLArray)
     a[Block(2, 2)] = randn(elt, 3, 3)
     @test a[2:4, 4] == Array(a)[2:4, 4]
     # TODO: Fix this.
-    @test_broken a[4, 2:4] == Array(a)[4, 2:4]
+    @test a[4, 2:4] == Array(a)[4, 2:4] broken = true
 end

test/test_tensoralgebraext.jl

@@ -2,7 +2,7 @@ using BlockArrays: Block, BlockArray, blockedrange, blocksize
 using BlockSparseArrays: BlockSparseArray
 using Random: randn!
 using TensorAlgebra: contract
-using Test: @test, @test_broken, @testset
+using Test: @test, @testset
 
 function randn_blockdiagonal(elt::Type, axes::Tuple)
     a = BlockSparseArray{elt}(undef, axes)