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ISSN: 0098-5589
Alex Groce , Oregon State University, Corvallis
Todd Kulesza , Oregon State University, Corvallis
Chaoqiang Zhang , Oregon State University, Corvallis
Shalini Shamasunder , Oregon State University, Corvallis
Margaret Burnett , Oregon State University, Corvallis
Weng-Keen Wong , Oregon State University, Corvallis
Simone Stumpf , City University London, London
Shubhomoy Das , Oregon State University, Corvallis
Amber Shinsel , Oregon State University, Corvallis
Forrest Bice , Oregon State University, Corvallis
Kevin McIntosh , Oregon State University, Corvallis
How do you test a program when only a single user, with no expertise in software testing, is able to determine if the program is performing correctly? Such programs are common today in the form of machine-learned classifiers. We consider the problem of testing this common kind of machine generated program when the only oracle is an end user: e.g., only you can determine if your email is properly filed. We present test selection methods that provide very good failure rates even for small test suites, and show that these methods work in both large-scale random experiments using a "gold standard" and in studies with real users. Our methods are inexpensive and largely algorithm-independent. Key to our methods is an exploitation of properties of classifiers that is not possible in traditional software testing. Our results suggest that it is plausible for time-pressured end users to interactively detect failures--even very hard-to-find failures--without wading through a large number of successful (and thus less useful) tests. We additionally show that some methods are able to find the arguably most difficult-to-detect faults of classifiers: cases where machine learning algorithms have high confidence in an incorrect result.
Machine learning, Testing and Debugging
Alex Groce, Todd Kulesza, Chaoqiang Zhang, Shalini Shamasunder, Margaret Burnett, Weng-Keen Wong, Simone Stumpf, Shubhomoy Das, Amber Shinsel, Forrest Bice, Kevin McIntosh, "You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems", IEEE Transactions on Software Engineering, vol. , no. , pp. 0, 5555, doi:10.1109/TSE.2013.59
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