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Thomas A. Grossman

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Software Engineering Artificial Intelligence Human-Computer Interaction

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preprint2026arXiv

Spreadsheet Modeling Experiments Using GPTs on Small Problem Statements and the Wall Task

This paper investigates how GPT-based tools can assist in building reusable analytical spreadsheet models. After a screening, we evaluate five GPT extensions and select Excel AI by pulsrai.com for detailed testing. Through structured experiments on simple problem statements, we assess Excel AI's performance against the ERFR criteria (each input in a cell; cell formulas; no hardwired numbers; labels; accurate). Results show that while Excel AI can produce well-structured models, it is inconsistent and often non-reproducible. We identify two central challenges - "the problem of confidence" and "the problem of workflow" - which highlight the need for skilled users to verify and adapt GPT-generated spreadsheets. Though GPTs show promise for generating draft models that may reduce development time or lower skill requirements, current tools remain unreliable for professional use. We conclude with recommendations for future research into prompt engineering, reproducibility, and larger-scale modeling tasks.

preprint2011arXiv

Towards Evaluating the Quality of a Spreadsheet: The Case of the Analytical Spreadsheet Model

We consider the challenge of creating guidelines to evaluate the quality of a spreadsheet model. We suggest four principles. First, state the domain-the spreadsheets to which the guidelines apply. Second, distinguish between the process by which a spreadsheet is constructed from the resulting spreadsheet artifact. Third, guidelines should be written in terms of the artifact, independent of the process. Fourth, the meaning of "quality" must be defined. We illustrate these principles with an example. We define the domain of "analytical spreadsheet models", which are used in business, finance, engineering, and science. We propose for discussion a framework and terminology for evaluating the quality of analytical spreadsheet models. This framework categorizes and generalizes the findings of previous work on the more narrow domain of financial spreadsheet models. We suggest that the ultimate goal is a set of guidelines for an evaluator, and a checklist for a developer.

preprint2010arXiv

Spreadsheets Grow Up: Three Spreadsheet Engineering Methodologies for Large Financial Planning Models

Many large financial planning models are written in a spreadsheet programming language (usually Microsoft Excel) and deployed as a spreadsheet application. Three groups, FAST Alliance, Operis Group, and BPM Analytics (under the name "Spreadsheet Standards Review Board") have independently promulgated standardized processes for efficiently building such models. These spreadsheet engineering methodologies provide detailed guidance on design, construction process, and quality control. We summarize and compare these methodologies. They share many design practices, and standardized, mechanistic procedures to construct spreadsheets. We learned that a written book or standards document is by itself insufficient to understand a methodology. These methodologies represent a professionalization of spreadsheet programming, and can provide a means to debug a spreadsheet that contains errors. We find credible the assertion that these spreadsheet engineering methodologies provide enhanced productivity, accuracy and maintainability for large financial planning models

preprint2009arXiv

The Lookup Technique to Replace Nested-IF Formulas in Spreadsheet Programming

Spreadsheet programmers often implement contingent logic using a nested-IF formula even though this technique is difficult to test and audit and is believed to be risky. We interpret the programming of contingent logic in spreadsheets in the context of traditional computer programming. We investigate the "lookup technique" as an alternative to nested-IF formulas, describe its benefits for testing and auditing, and define its limitations. The lookup technique employs four distinct principles: 1) make logical tests visible; 2) make outcomes visible; 3) make logical structure visible; and 4) replace a multi-function nested-IF formula with a single-function lookup formula. It can be used only for certain simple contingent logic. We describe how the principles can be applied in more complex situations, and suggest avenues for further research.