Log In Sign Up

Combining Spreadsheet Smells for Improved Fault Prediction

by   Patrick Koch, et al.

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.


page 1

page 2

page 3

page 4


IBIR: Bug Report driven Fault Injection

Much research on software engineering and software testing relies on exp...

Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?

IS faults present milder symptoms compared to severe faults, and are mor...

An empirical study of Linespots: A novel past-fault algorithm

This paper proposes the novel past-faults fault prediction algorithm Lin...

Futuristic Classification with Dynamic Reference Frame Strategy

Classification is one of the widely used analytical techniques in data s...

Revisiting the size effect in software fault prediction models

BACKGROUND: In object oriented (OO) software systems, class size has bee...

Identifying the root cause of cable network problems with machine learning

Good quality network connectivity is ever more important. For hybrid fib...