Visual Checking of Spreadsheets

The difference between surface and deep structures of a spreadsheet is a major cause of difficulty in checking spreadsheets. After a brief survey of current methods of checking (or debugging) spreadsheets, new visual methods of showing the deep structures are presented. Illustrations are given on how these visual methods can be employed in various interactive local and global debugging strategies.Comment: 11 Pages, 5 Figure

Gencel : a program generator for correct spreadsheets

A huge discrepancy between theory and practice exists in one popular application area of functional programming--spreadsheets. Although spreadsheets are the most frequently used (functional) programs, few formal models of computation and type systems exist that would provide the foundation for creating reliable spreadsheets. Consequently, existing spreadsheets contain many errors, some of which have serious impacts. We argue in favor of creating a formal foundation for spreadsheets to help improve spreadsheet systems and make spreadsheets more reliable software assets. To this end, we have developed a table specification language that allows the definition of table templates that describe possible spreadsheet evolutions. This language is based on a table calculus that formally captures the process of creating and modifying spreadsheets. We have developed a type system for this calculus that can prevent type, reference, and omission errors from occurring in spreadsheets. On the basis of the table calculus we have developed Gencel, a system for generating reliable spreadsheets. We have implemented a prototype version of Gencel as an extension of Excel. A pilot study has indicated that the system is well accepted by end-user programmers

Protecting Spreadsheets Against Fraud

Previous research on spreadsheet risks has predominantly focussed on errors inadvertently introduced by spreadsheet writers i.e. it focussed on the end-user aspects of spreadsheet development. When analyzing a faulty spreadsheet, one might not be able to determine whether a particular error (fault) has been made by mistake or with fraudulent intentions. However, the fences protecting against fraudulent errors have to be different from those shielding against inadvertent mistakes. Faults resulting from errors committed inadvertently can be prevented ab initio by tools that notify the spreadsheet writer about potential problems whereas faults that are introduced on purpose have to be discovered by auditors without the cooperation of their originators. Even worse, some spreadsheet writers will do their best to conceal fraudulent parts of their spreadsheets from auditors. In this paper we survey the available means for fraud protection by contrasting approaches suitable for spreadsheets with those known from fraud protection for conventional software.Comment: 16 Pages including extensive reference

On the Refinement of Spreadsheet Smells by means of Structure Information

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are more likely to be the root source of bugs. Unfortunately, current spreadsheet smell detection techniques suffer from a number of drawbacks that lead to incorrect or redundant smell reports. For example, the same quality issue is often reported for every copy of a cell, which may overwhelm users. To deal with these issues, we propose to refine spreadsheet smells by exploiting inferred structural information for smell detection. We therefore first provide a detailed description of our static analysis approach to infer clusters and blocks of related cells. We then elaborate on how to improve existing smells by providing three example refinements of existing smells that incorporate information about cell groups and computation blocks. Furthermore, we propose three novel smell detection techniques that make use of the inferred spreadsheet structures. Empirical evaluation of the proposed techniques suggests that the refinements successfully reduce the number of incorrectly and redundantly reported smells, and novel deficits are revealed by the newly introduced smells

Training Gamble leads to Corporate Grumble?

Fifteen years of research studies have concluded unanimously that spreadsheet errors are both common and non-trivial. Now we must seek ways to reduce spreadsheet errors. Several approaches have been suggested, some of which are promising and others, while appealing because they are easy to do, are not likely to be effective. To date, only one technique, cell-by-cell code inspection, has been demonstrated to be effective. We need to conduct further research to determine the degree to which other techniques can reduce spreadsheet errors.Comment: 8 Pages, 4 Figure

A Spreadsheet Auditing Tool Evaluated in an Industrial Context

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.Comment: 12 Pages, 2 Figures, 4 Table

A Toolkit for Scalable Spreadsheet Visualization

This paper presents a toolkit for spreadsheet visualization based on logical areas, semantic classes and data modules. Logical areas, semantic classes and data modules are abstract representations of spreadsheet programs that are meant to reduce the auditing and comprehension effort, especially for large and regular spreadsheets. The toolkit is integrated as a plug-in in the Gnumeric spreadsheet system for Linux. It can process large, industry scale spreadsheet programs in reasonable time and is tightly integrated with its host spreadsheet system. Users can generate hierarchical and graph-based representations of their spreadsheets. This allows them to spot conceptual similarities in different regions of the spreadsheet, that would otherwise not fit on a screen. As it is assumed that the learning effort for effective use of such a tool should be kept low, we aim for intuitive handling of most of the tool's functions.Comment: 12 Page

Ensuring Spreadsheet Integrity with Model Master

We have developed the Model Master (MM) language for describing spreadsheets, and tools for converting MM programs to and from spreadsheets. The MM decompiler translates a spreadsheet into an MM program which gives a concise summary of its calculations, layout, and styling. This is valuable when trying to understand spreadsheets one has not seen before, and when checking for errors. The MM compiler goes the other way, translating an MM program into a spreadsheet. This makes possible a new style of development, in which spreadsheets are generated from textual specifications. This can reduce error rates compared to working directly with the raw spreadsheet, and gives important facilities for code reuse. MM programs also offer advantages over Excel files for the interchange of spreadsheets.Comment: 15 pages; substantive references; code example

EuSpRIG TEAM work:Tools, Education, Audit, Management

Research on spreadsheet errors began over fifteen years ago. During that time, there has been ample evidence demonstrating that spreadsheet errors are common and nontrivial. Quite simply, spreadsheet error rates are comparable to error rates in other human cognitive activities and are caused by fundamental limitations in human cognition, not mere sloppiness. Nor does ordinary "being careful" eliminate errors or reduce them to acceptable levels.Comment: 7 Pages, 1 Figur

Now You're Thinking With Structures: A Concept for Structure-based Interactions with Spreadsheets

Spreadsheets are the go-to tool for computerized calculation and modelling, but are hard to comprehend and adapt after reaching a certain complexity. In general, cognition of complex systems is facilitated by having a higher order mental model of the system in question to work with. We therefore present a concept for structure-aware understanding of and interaction with spreadsheets that extends previous work on structure inference in the domain. Following this concept, structural information is used to enrich visualizations, reactively enhance traditional user actions, and provide tools to proactively alter the overall spreadsheet makeup instead of individual cells The intended systems should, in first approximation, not replace common spreadsheet tools, but provide an additional layer of functionality alongside the established interface. In ongoing work, we therefore implemented a tool for structure inference and visualization along the common spreadsheet layout. Based on this framework, we plan to introduce the envisioned proactive and reactive interaction mechanics, and finally provide structure-aware unctionality as an add-in for common spreadsheet processors. We believe that providing the tools for thinking about and interacting with spreadsheets in this manner will benefit users both in terms of productivity and overall spreadsheet quality.Comment: In Proceedings of the 5th International Workshop on Software Engineering Methods in Spreadsheets (arXiv:1808.09174