  Although feature models are widely used in practice, for example,
representing variability in software product lines, their integration is still
a challenge. Many integration techniques have been proposed, although none of
these have proven to be fully effective. Integrating feature models becomes a
difficult, costly, error-prone task. Since their transition occurs in a
generalized and automated way, the techniques applied to compose the models end
up giving rise to a final model, in many cases undesired, without taking into
account the specific needs arising from the requirements determined by the
analysts and developers. Therefore, this work proposes FMIT, a technique for
integrating feature models. The FMIT is based on contemporary model integration
strategies to increase the accuracy and quality of the integrated feature
model. In this way, it will be possible to identify the degree of similarity
between composite feature diagrams, to verify their accuracy, as well as to
identify conflicts. In addition, this work proposes the development of a
prototype based on the set of strategies, used to take decisions according to
the requirements established during the integration of feature models, whether
this is semi-automatic or automatic. To evaluate FMIT, experimental studies
were conducted with 10 participants, including students and professionals.
Participants performed 12 integration scenarios, 6 using the FMIT and 6
manually. The results suggest that FMIT improved accuracy by 43\% of the cases,
as well as reduced the effort by 70\% to perform the integrations.
