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Building meaningful interoperation with external software units requiresperforming the conceptual interoperability analysis that starts withidentifying 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 ofdifferent types including structure, dynamic, and quality. Missing suchconstraints may lead to unexpected mismatches, expensive resolution, andrunning-late projects. However, it is a challenging task for softwarearchitects and analysts to manually analyze the unstructured text in APIdocuments to identify the COINs. Not only it is a tedious and time-consumingtask, but also it needs knowledge about the constraint types. In this article,we present and evaluate our idea of utilizing machine learning techniques inautomating the COIN identification, which is the first step of conceptualinteroperability analysis, from human text in API documents. Our empiricalresearch started with a multiple-case study to build the ground truth dataset,on which we contributed our machine learning COIN-Classification Model. We showthe model's robustness through experiments using different machine learningtext-classification algorithms. The experiments' results revealed that ourmodel can achieve up to 87% accuracy in automatically identifying the COINs intext. Thus, we implemented a tool that embeds our model to demonstrate itspractical value in industrial context. Then, we evaluated the practitioners'acceptance for the tool and found that they significantly agreed on itsusefulness and ease of use.

How practical is it? Machine Learning for Identifying Conceptual Interoperability Constraints in API Documents