  In the past two decades, several Machine Learning (ML) libraries have become
freely available. Many studies have used such libraries to carry out empirical
investigations on predictive Software Engineering (SE) tasks. However, the
differences stemming from using one library over another have been overlooked,
implicitly assuming that using any of these libraries would provide the user
with the same or very similar results. This paper aims at raising awareness of
the differences incurred when using different ML libraries for software
development effort estimation (SEE), one of most widely studied SE prediction
tasks. To this end, we investigate 4 deterministic machine learners as provided
by 3 of the most popular ML open-source libraries written in different
languages (namely, Scikit-Learn, Caret and Weka). We carry out a thorough
empirical study comparing the performance of the machine learners on 5 SEE
datasets in the two most common SEE scenarios (i.e., out-of-the-box-ml and
tuned-ml) as well as an in-depth analysis of the documentation and code of
their APIs. The results of our study reveal that the predictions provided by
the 3 libraries differ in 95% of the cases on average across a total of 105
cases studied. These differences are significantly large in most cases and
yield misestimations of up to approx. 3,000 hours per project. Moreover, our
API analysis reveals that these libraries provide the user with different
levels of control on the parameters one can manipulate, and a lack of clarity
and consistency, overall, which might mislead users. Our findings highlight
that the ML library is an important design choice for SEE studies, which can
lead to a difference in performance. However, such a difference is
under-documented. We conclude by highlighting open-challenges with suggestions
for the developers of libraries as well as for the researchers and
practitioners using them.

Software Engineering;Machine Learning