Home

An Approach to Software Testing of Machine Learning Applications

Christian Murphy; Gail E. Kaiser; Marta Arias

Title:
An Approach to Software Testing of Machine Learning Applications
Author(s):
Murphy, Christian
Kaiser, Gail E.
Arias, Marta
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-014-07
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
Some machine learning applications are intended to learn properties of data sets where the correct answers are not already known to human users. It is challenging to test such ML software, because there is no reliable test oracle. We describe a software testing approach aimed at addressing this problem. We present our findings from testing implementations of two different ML ranking algorithms: Support Vector Machines and MartiRank.
Subject(s):
Computer science
Item views:
269
Metadata:
text | xml

In Partnership with the Center for Digital Research and Scholarship at Columbia University Libraries/Information Services | Terms of Use