A Scalable Method for Deductive Generalization in the Spreadsheet Paradigm    

In this paper, we present an efficient method for automatically generalizing programs written in spreadsheet languages. The strategy is to do generalization through incremental analysis of logical relationships among concrete program entities from the perspective of a particular computational goal. The method uses deductive dataflow analysis with algebraic back-substitution rather than inference with heuristics, and there is no need for generalization-related dialog with the user. We present the algorithms and their time complexities and show that, because the algorithms perform their analyses incrementally, on only the on-screen program elements rather than on the entire program, the method is scalable. Performance data is presented to help demonstrate the scalability.

Burnett, Margaret, Sherry Yang and Jay Summet. ACM TOCHI (2002). Articles>Human Computer Interaction