Shape-derivatives combined with the adjoint approach offer an efficient approach for solving shape optimisation problems, but their derivation is error-prone especially for complex PDEs. In this talk, we present an algorithmic differentiation tool that automatically computes shape derivatives by aexploiting the symbolic representation of variational problems in the finite element framework FEniCS. We demonstrate that our approach computes first and second order shape derivatives for a wide range of PDEs and functionals, approaches optimal performance and works naturally in parallel.