An Approach to Software Testing of Machine Learning Applications
Christian Murphy, Gail E. Kaiser, Marta Arias · 2007
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.