  Context: Conducting experiments is central to research machine learning
research to benchmark, evaluate and compare learning algorithms. Consequently
it is important we conduct reliable, trustworthy experiments. Objective: We
investigate the incidence of errors in a sample of machine learning experiments
in the domain of software defect prediction. Our focus is simple arithmetical
and statistical errors. Method: We analyse 49 papers describing 2456 individual
experimental results from a previously undertaken systematic review comparing
supervised and unsupervised defect prediction classifiers. We extract the
confusion matrices and test for relevant constraints, e.g., the marginal
probabilities must sum to one. We also check for multiple statistical
significance testing errors. Results: We find that a total of 22 out of 49
papers contain demonstrable errors. Of these 7 were statistical and 16 related
to confusion matrix inconsistency (one paper contained both classes of error).
Conclusions: Whilst some errors may be of a relatively trivial nature, e.g.,
transcription errors their presence does not engender confidence. We strongly
urge researchers to follow open science principles so errors can be more easily
be detected and corrected, thus as a community reduce this worryingly high
error rate with our computational experiments.
