diff --git a/tests/linalg/eigen.rs b/tests/linalg/eigen.rs
index 3f9ac097..0bcd672e 100644
--- a/tests/linalg/eigen.rs
+++ b/tests/linalg/eigen.rs
@@ -1,4 +1,5 @@
-t
+#![cfg_attr(rustfmt, rustfmt_skip)]
+
 use na::DMatrix;
 
 #[cfg(feature = "arbitrary")]
@@ -103,89 +104,3 @@ fn symmetric_eigen_singular_24x24() {
         epsilon = 1.0e-5
     ));
 }
-
-//  #[cfg(feature = "arbitrary")]
-//  quickcheck! {
-// FIXME: full eigendecomposition is not implemented yet because of its complexity when some
-// eigenvalues have multiplicity > 1.
-//
-//    /*
-//     * NOTE: for the following tests, we use only upper-triangular matrices.
-//     * Thes ensures the schur decomposition will work, and allows use to test the eigenvector
-//     * computation.
-//     */
-//    fn eigen(n: usize) -> bool {
-//        let n = cmp::max(1, cmp::min(n, 10));
-//        let m = DMatrix::<f64>::new_random(n, n).upper_triangle();
-//
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//
-//    fn eigen_with_adjascent_duplicate_diagonals(n: usize) -> bool {
-//        let n = cmp::max(1, cmp::min(n, 10));
-//        let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
-//
-//        // Suplicate some adjascent diagonal elements.
-//        for i in 0 .. n / 2 {
-//            m[(i * 2 + 1, i * 2 + 1)] = m[(i * 2, i * 2)];
-//        }
-//
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//
-//    fn eigen_with_nonadjascent_duplicate_diagonals(n: usize) -> bool {
-//        let n = cmp::max(3, cmp::min(n, 10));
-//        let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
-//
-//        // Suplicate some diagonal elements.
-//        for i in n / 2 .. n {
-//            m[(i, i)] = m[(i - n / 2, i - n / 2)];
-//        }
-//
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//
-//    fn eigen_static_square_4x4(m: Matrix4<f64>) -> bool {
-//        let m = m.upper_triangle();
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//
-//    fn eigen_static_square_3x3(m: Matrix3<f64>) -> bool {
-//        let m = m.upper_triangle();
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//
-//    fn eigen_static_square_2x2(m: Matrix2<f64>) -> bool {
-//        let m = m.upper_triangle();
-//        println!("{}", m);
-//        let eig = RealEigen::new(m.clone()).unwrap();
-//        verify_eigenvectors(m, eig)
-//    }
-//  }
-//
-// fn verify_eigenvectors<D: Dim>(m: MatrixN<f64, D>, mut eig: RealEigen<f64, D>) -> bool
-//     where DefaultAllocator: Allocator<f64, D, D>   +
-//                             Allocator<f64, D>      +
-//                             Allocator<usize, D, D> +
-//                             Allocator<usize, D>,
-//           MatrixN<f64, D>: Display,
-//           VectorN<f64, D>: Display {
-//     let mv = &m * &eig.eigenvectors;
-//
-//     println!("eigenvalues: {}eigenvectors: {}", eig.eigenvalues, eig.eigenvectors);
-//
-//     let dim = m.nrows();
-//     for i in 0 .. dim {
-//         let mut col = eig.eigenvectors.column_mut(i);
-//         col *= eig.eigenvalues[i];
-//     }
-//
-//     println!("{}{:.5}{:.5}", m, mv, eig.eigenvectors);
-//
-//     relative_eq!(eig.eigenvectors, mv, epsilon = 1.0e-5)
-// }