An Approach to Software Testing of Machine Learning Applications
- An Approach to Software Testing of Machine Learning Applications
- Murphy, Christian
Kaiser, Gail E.
- Technical reports
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- 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.
- Computer science
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- Suggested Citation:
- Christian Murphy, Gail E. Kaiser, Marta Arias, 2007, An Approach to Software Testing of Machine Learning Applications, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:29502.