In recent years several tools and methodologies have been developed to improve the dependability of spreadsheets. However, there has been little evaluation of these dependability devices on spreadsheets in actual use by end users. To assist in the process of evaluating these methodologies, we have assembled a corpus of spreadsheets from a variety of sources. We have ensured that these spreadsheets are suitable for evaluating dependability devices in Microsoft Excel (the most commonly used commercial spreadsheet environment) and have measured a variety of feature of these spreadsheets to aid researchers in selecting subsets of the corpus appropriate to their needs.

Spreadsheets are a common tool in end-user programming. But even while important decisions are based on spreadsheet computations, spreadsheets are poorly documented software and the differences between simple on-shot computations and large, long-living sheets are not well understood. Like other software, production spreadsheets are subject to repeated maintenance cycles. Consequently, as with conventional software, short maintenance cycles and poor documentation tend to decrease their quality. We introduce an approach to help maintainers understand the structure of large spreadsheets as well as to zoom into certain parts of the spreadsheet. To cope with large sheets, our approach features two levels of abstraction: logical areas and semantic classes. These abstractions are based on different degrees of relatedness of cells according to the formulas they contain.

Although researchers have developed several ways to reason about the location of faults in spreadsheets, no single form of reasoning is without limitations. Multiple types of errors can appear in spreadsheets, and various fault localization techniques differ in the kinds of errors that they are effective in locating. In this paper, we report empirical results from an emerging system that attempts to improve fault localization for end-user programmers by sharing the results of the reasoning systems found in WYSIWYT and UCheck. By evaluating the visual feedback from each fault localization system, we shed light on where these different forms of reasoning and combinations of them complement — and contradict — one another, and which heuristics can be used to generate the best advice from a combination of these systems. 1.

Amongst the large number of write-and-throw-away-spreadsheets developed for one-time use there is a rather neglected proportion of spreadsheets that are huge, periodically used, and submitted to regular update-cycles like any conventionally evolving valuable legacy application software. However, due to the very nature of spreadsheets, their evolution is particularly tricky and therefore error-prone. In our strive to develop tools and methodologies to improve spreadsheet quality, we analysed consolidation spreadsheets of an internationally operating company for the errors they contain. The paper presents the results of the field audit, involving 78 spreadsheets with 60,446 non-empty cells. As a by-product, the study performed was also to validate our analysis tools in an industrial context. The evaluated auditing tool offers the auditor a new view on the formula structure of the spreadsheet by grouping similar formulas into equivalence classes. Our auditing approach defines three similarity criteria between formulae, namely copy, logical and structural equivalence. To improve the visualization of large spreadsheets, equivalences and data dependencies are displayed in separated windows that are interlinked with the spreadsheet. The auditing approach helps to find irregularities in the geometrical pattern of similar formulas. 1

Spreadsheets are an extremely common form of end-user programming used for many applications from student marks to accounting for global multinationals. Ways of studying the structure of a spreadsheet itself is normally constrained to the tools provided in the spreadsheet software. We wanted to explore ways to use new visualisations for spreadsheets, and this paper documents our approach .

Abstract approved: