Although there has been recent research into ways to design environments that enable end users to create their own programs, little attention has been given to helping these end users systematically test their programs. To help address this need in spreadsheet systems (the most widely used type of end-user programming language), we previously introduced a visual approach to systematically testing individual cells in spreadsheet systems. However, the previous approach did not scale well in the presence of largely homogeneous grids, which introduce problems somewhat analogous to the array-testing problems of imperative programs. We present two approaches to spreadsheet testing that explicitly support such grids. We present the algorithms, time complexities, and performance data comparing the two approaches. This is part of our continuing work to bring to end users at least some of the benefits of formalized notions of testing without requiring knowledge of testing beyond a naive level.

Spreadsheet languages, which include commercial spreadsheets and various research systems, have had a substantial impact on end-user computing. Research shows, however, that spreadsheets often contain faults; thus, we would like to provide at least some of the benefits of formal testing methodologies to the creators of spreadsheets. This article presents a testing methodology that adapts data flow adequacy criteria and coverage monitoring to the task of testing spreadsheets. To accommodate the evaluation model used with spreadsheets, and the interactive process by which they are created, our methodology is incremental. To accommodate the users of spreadsheet languages, we provide an interface to our methodology that does not require an understanding of testing theory. We have implemented our testing methodology in the context of the Forms/3 visual spreadsheet language. We report on the methodology, its time and space costs, and the mapping from the testing strategy to the user interface. In an empirical study, we found that test suites created according to our methodology detected, on average, 81% of the faults in a set of faulty spreadsheets, significantly outperforming randomly generated test suites.