Abstract
Machine Learning (ML) based solutions are becoming increasingly popular and pervasive. When testing such solutions, there is a tendency to focus on improving the ML metrics such as the F1-score and accuracy at the expense of ensuring business value and correctness by covering business requirements. In this work, we adapt test planning methods of classical software to ML solutions. We use combinatorial modeling methodology to define the space of business requirements and map it to the ML solution data, and use the notion of data slices to identify the weaker areas of the ML solution and strengthen them. We apply our approach to three real-world case studies and demonstrate its value.
| Original language | English |
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| Title of host publication | ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
| Editors | Sven Apel, Marlon Dumas, Alessandra Russo, Dietmar Pfahl |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1048-1058 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781450355728 |
| DOIs | |
| State | Published - 12 Aug 2019 |
| Externally published | Yes |
| Event | 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 - Tallinn, Estonia Duration: 26 Aug 2019 → 30 Aug 2019 |
Publication series
| Name | ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
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Conference
| Conference | 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 |
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| Country/Territory | Estonia |
| City | Tallinn |
| Period | 26/08/19 → 30/08/19 |
Bibliographical note
Publisher Copyright:© 2019 ACM.
Keywords
- Combinatorial Modeling
- Machine Learning
- Software Testing