I will address issues in the application of symbolic computation to surface analysis. Until quite recently, the most important factors affecting the style of technical software have been the limitations imposed by the speed and storage capacity of contemporary computing hardware. In retrospect, while understandable in the context of the times, this linkage has produced computational tools that lack generality, are inflexible, or that must be frequently updated because of evolving computer hardware or operating system software. The seeds of an alternative approach have been sown by the authors of modern tools for general-purpose symbolic mathematical computation, where fundamental considerations argue for hardware independence and the generality of algorithms. Symbolic computation is a revolutionary computing technology. Mathematics is an exercise in discovering patterns and manipulating symbols according to complex and exceedingly numerous, but well defined and self-consistent rules. When advances in computer speed and memory capacity made it possible to store and implement these rules automatically, the stage was set for a revolution on a scale comparable to the revolution produced by automatic numerical computation five decades ago. Some implications of this revolution for the field of surface analysis will be presented, through examples drawn from medium energy backscattering spectrometry, four-point probe measurements, and radiation effects in semiconductors. The distinctive properties of an extensible surface analyst's quantitative workbench will be discussed. An important conclusion is that most technical software now being written should be based on robust algorithms and fidelity to correct physics without (much) regard for the characteristics of the hardware on which it will initially be implemented. @FootnoteText@ This work has been supported in part by the U.S. Army Research Office through grant DAAD 19-99-1-0283.