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.
Original language | English |
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Title of host publication | Proceedings 2018 ACM/IEEE 40th International Conference on Software Engineering |
Subtitle of host publication | New Ideas and Emerging Results, ICSE-NIER 2018 |
Publisher | IEEE Computer Society, 1998 |
Pages | 25-28 |
Number of pages | 4 |
ISBN (Electronic) | 9781450356626 |
DOIs | |
Publication status | Published - 27 May 2018 |
Event | 40th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2018 - Gothenburg, Sweden Duration: 30 May 2018 → 1 Jun 2018 |
Conference
Conference | 40th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2018 |
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Country/Territory | Sweden |
City | Gothenburg |
Period | 30/05/18 → 1/06/18 |
Keywords
- Fault Prediction
- Spreadsheet QA
- Spreadsheet Smells
ASJC Scopus subject areas
- Software