An Approach to Software Testing of Machine Learning Applications

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.

• Type: Conference
• Year: 2007
• Where: SEKE
• Authors: Chris Murphy, Gail E. Kaiser, Marta Arias
• Keywords: Machine Learning Applications, ML Ranking Algorithms, Reliable Test Oracle, SEKE 2007