Complexity Metrics for Spreadsheet Models

TL;DR

This paper introduces new complexity metrics for spreadsheet models focusing on cell references and paths, aiming to identify error-prone cells and improve model reliability and understandability.

Contribution

It defines novel complexity metrics related to spreadsheet structure, adapting some from software engineering, to assess error risk and aid in model evaluation.

Findings

Metrics help identify cells prone to errors

Metrics can estimate development effort and reliability

Concepts like reference branching improve model quality

Abstract

Several complexity metrics are described which are related to logic structure, data structure and size of spreadsheet models. They primarily concentrate on the dispersion of cell references and cell paths. Most metrics are newly defined, while some are adapted from traditional software engineering. Their purpose is the identification of cells which are liable to errors. In addition, they can be used to estimate the values of dependent process metrics, such as the development duration and effort, and especially to adjust the cell error rate in accordance with the contents of each individual cell, in order to accurately asses the reliability of a model. Finally, two conceptual constructs - the reference branching condition cell and the condition block - are discussed, aiming at improving the reliability, modifiability, auditability and comprehensibility of logical tests.