Higher-order lazy functional slicing

Program slicing is a well known family of techniques intended to identify and isolate code fragments which depend on, or are depended upon, specific program entities. This is particularly useful in the areas of reverse engineering, program understanding, testing and software maintenance. Most slicing methods, and corresponding tools, target either the imperative or the object oriented paradigms, where program slices are computed with respect to a variable or a program statement. Taking a complementary point of view, this paper focuses on the slicing of higher-order functional programs under a lazy evaluation strategy. A prototype of a Haskell slicer, built as proof-of-concept for these ideas, is also introduced

Error bounds for low-rank approximations of the first exponential integral kernel

A hierarchical matrix is an efficient data-sparse representation of a matrix, especially useful for large dimensional problems. It consists of low-rank subblocks leading to low memory requirements as well as inexpensive computational costs. In this work, we discuss the use of the hierarchical matrix technique in the numerical solution of a large scale eigenvalue problem arising from a finite rank discretization of an integral operator. The operator is of convolution type, it is defined through the first exponential-integral function and, hence, it is weakly singular. We develop analytical expressions for the approximate degenerate kernels and deduce error upper bounds for these approximations. Some computational results illustrating the efficiency and robustness of the approach are presented.