Combining Spreadsheet Smells for Improved Fault Prediction

TL;DR

This paper introduces a machine learning approach that combines multiple spreadsheet smell indicators using AdaBoost to improve fault prediction accuracy in spreadsheets, addressing the limitations of individual smell-based predictions.

Contribution

The study proposes a novel ensemble method that integrates various spreadsheet smells for enhanced fault prediction, demonstrating significant accuracy improvements.

Findings

Improved fault prediction accuracy with ensemble approach

Combining multiple smells outperforms individual smell predictions

Effective on real-world spreadsheet fault datasets

Abstract

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.