  [Context] The use of defect prediction models, such as classifiers, can
support testing resource allocations by using data of the previous releases of
the same project for predicting which software components are likely to be
defective. A validation technique, hereinafter technique defines a specific way
to split available data in training and test sets to measure a classifier
accuracy. Time-series techniques have the unique ability to preserve the
temporal order of data; i.e., preventing the testing set to have data
antecedent to the training set. [Aim] The aim of this paper is twofold: first
we check if there is a difference in the classifiers accuracy measured by
time-series versus non-time-series techniques. Afterward, we check for a
possible reason for this difference, i.e., if defect rates change across
releases of a project. [Method] Our method consists of measuring the accuracy,
i.e., AUC, of 10 classifiers on 13 open and two closed projects by using three
validation techniques, namely cross validation, bootstrap, and walk-forward,
where only the latter is a time-series technique. [Results] We find that the
AUC of the same classifier used on the same project and measured by 10-fold
varies compared to when measured by walk-forward in the range [-0.20, 0.22],
and it is statistically different in 45% of the cases. Similarly, the AUC
measured by bootstrap varies compared to when measured by walk-forward in the
range [-0.17, 0.43], and it is statistically different in 56% of the cases.
[Conclusions] We recommend choosing the technique to be used by carefully
considering the conclusions to draw, the property of the available datasets,
and the level of realism with the classifier usage scenario.
