Spreadsheet debugging: The perils of tool over-reliance

Mukhtar, Adil; Hofer, Birgit; Jannach, Dietmar; Wotawa, Franz

doi:10.1016/j.jss.2021.111119

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…Since spreadsheets are, in most cases, created by end-users who are self-taught and have no education in software engineering, the error rate is assumed higher than in traditional software [3]. An experimentbased study on spreadsheet development reports that more than 95% of spreadsheets contain at least one error [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Automated Testing Tools for Spreadsheets

علي أبوراس

2022

JOPAS

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Organizations and industries use spreadsheet programs, such as MS Excel, for various tasks, such as accounting, financial calculations, and reporting. However, the number of spreadsheets that contain errors is very high. One primary reason is that different end-users develop spreadsheets without programming or software development training. Thus, researchers have proposed a variety of automated tools and techniques to support the end-users in finding and fixing errors in spreadsheets. The main objective of this paper is to study different automated tools in spreadsheet testing and classify them according to two criteria: how they work and the ability to find spreadsheet faults. For this purpose, we discuss various spreadsheet testing automated tools, techniques, and strategies. In addition, we highlight their performance and where they can be used so that they can be beneficial to both end-users and researchers. We then set out key directions for potential future works.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Automated Testing Tools for Spreadsheets

علي أبوراس

2022

JOPAS

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“…This rate can be reduced further depending on the underlying quality assurance techniques and processes; for example, Microsoft reports 0.5 defects per 1000 lines of code in released products. The error rate in spreadsheets, on the other hand, is thought to be around 3-5% [2].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Usefulness of Metric-based Prediction Method for Spreadsheet Fault Detection

Kunya,

Hamada,

Hassan

et al. 2022

SHS Web Conf.

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The ability to predict whether a specific section of a spreadsheet is faulty or not is frequently required for the development of spreadsheet functionality. Although errors in such spreadsheets are common and can have serious consequences, today’s spreadsheet creation and management tools offer weak capabilities for defect detection, localization, and fixing. In this thesis, we proposed a method for predicting faults in spreadsheet formulas that can detect faults in non-formula cells by combining a catalog of spreadsheet metrics with modern machine learning algorithms. An examination of the individual metrics in the catalog reveals that they are suited to detecting data where a formula is expected to have flaws. In this framework, Recall Score of 99% was achieved and performance was compared with that of Melford. The result of the experiment reveals that the proposed framework outperforms Melford framework.

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