Bridging the gap between ML solutions and their business requirements using feature interactions

Guy Barash, Eitan Farchi, Ilan Jayaraman, Orna Raz, Rachel Tzoref-Brill, Marcel Zalmanovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

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 languageEnglish
Title of host publicationESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
EditorsSven Apel, Marlon Dumas, Alessandra Russo, Dietmar Pfahl
PublisherAssociation for Computing Machinery, Inc
Pages1048-1058
Number of pages11
ISBN (Electronic)9781450355728
DOIs
StatePublished - 12 Aug 2019
Externally publishedYes
Event27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 - Tallinn, Estonia
Duration: 26 Aug 201930 Aug 2019

Publication series

NameESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Conference

Conference27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019
Country/TerritoryEstonia
CityTallinn
Period26/08/1930/08/19

Bibliographical note

Publisher Copyright:
© 2019 ACM.

Keywords

  • Combinatorial Modeling
  • Machine Learning
  • Software Testing

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