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.
| Language | English |
|---|---|
| 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 | |
| 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 |
|---|---|
| Country | Sweden |
| City | Gothenburg |
| Period | 30/05/18 → 1/06/18 |
Fingerprint
Keywords
- Fault Prediction
- Spreadsheet QA
- Spreadsheet Smells
ASJC Scopus subject areas
- Software
Cite this
Combining spreadsheet smells for improved fault prediction. / Koch, Patrick; Schekotihin, Konstantin; Jannach, Dietmar; Hofer, Birgit; Wotawa, Franz; Schmitz, Thomas.
Proceedings 2018 ACM/IEEE 40th International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2018. IEEE Computer Society, 1998, 2018. p. 25-28.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Combining spreadsheet smells for improved fault prediction
AU - Koch, Patrick
AU - Schekotihin, Konstantin
AU - Jannach, Dietmar
AU - Hofer, Birgit
AU - Wotawa, Franz
AU - Schmitz, Thomas
PY - 2018/5/27
Y1 - 2018/5/27
N2 - 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.
AB - 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.
KW - Fault Prediction
KW - Spreadsheet QA
KW - Spreadsheet Smells
UR - http://www.scopus.com/inward/record.url?scp=85049772205&partnerID=8YFLogxK
U2 - 10.1145/3183399.3183402
DO - 10.1145/3183399.3183402
M3 - Conference contribution
SP - 25
EP - 28
BT - Proceedings 2018 ACM/IEEE 40th International Conference on Software Engineering
PB - IEEE Computer Society, 1998
ER -