SHS Web Conf.
Volume 139, 2022The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
|Number of page(s)||10|
|Section||Topics in Computer Science|
|Published online||13 May 2022|
Investigating the Usefulness of Metric-based Prediction Method for Spreadsheet Fault Detection
1 Department of Computer Science, Bayero University, Kano. Nigeria
2 University of Aizu, Japan
3 Department of Software Engineering, Bayero University, Kano. Nigeria
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.
Key words: Spreadsheet / Random Forest / Support Vector Machines / Deep Neural Networks / Adaptive Boosting / Fault Detection
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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