  Building meaningful interoperation with external software units requires
performing the conceptual interoperability analysis that starts with
identifying the conceptual interoperability constraints of each software unit,
then it compares the systems' constraints to detect their conceptual mismatch.
We call the conceptual interoperability constraints (the COINs) that can be of
different types including structure, dynamic, and quality. Missing such
constraints may lead to unexpected mismatches, expensive resolution, and
running-late projects. However, it is a challenging task for software
architects and analysts to manually analyze the unstructured text in API
documents to identify the COINs. Not only it is a tedious and time-consuming
task, but also it needs knowledge about the constraint types. In this article,
we present and evaluate our idea of utilizing machine learning techniques in
automating the COIN identification, which is the first step of conceptual
interoperability analysis, from human text in API documents. Our empirical
research started with a multiple-case study to build the ground truth dataset,
on which we contributed our machine learning COIN-Classification Model. We show
the model's robustness through experiments using different machine learning
text-classification algorithms. The experiments' results revealed that our
model can achieve up to 87% accuracy in automatically identifying the COINs in
text. Thus, we implemented a tool that embeds our model to demonstrate its
practical value in industrial context. Then, we evaluated the practitioners'
acceptance for the tool and found that they significantly agreed on its
usefulness and ease of use.
