Universidade Federal do Ceará
Mestrado e Doutorado em Ciências da Computação

Establishing interoperability links is a significant challenge in systems-of-information systems (SoIS) engineering. Even with constituent systems (CS) interfaces documentation, achieving such links is a difficult, time-consuming, and error-prone task that requires attention from CS developers, especially if it is performed manually. To contribute to that task, we propose a semi-automatic method using both syntactic and semantic similarity analysis of application programming interface (API) descriptions to identify potential connection points among CS. That method consists of three interconnected steps: (i) requirements definition, where we outline the motivations and justifications for creating the method; (ii) core method design, where we detail all the phases and components necessary to obtain the API connection points; and (iii) core method implementation, where we present the technological details of the tool developed to support the method. Through that tool, we perform two evaluations. In the first, we executed a controlled experiment to evaluate the tool’s performance and empirically define the best similarity algorithms and thresholds for the task of identifying connection points between two well-known API. In the second, we use the tool in a case study applied to a real-world SoIS of a global computer manufacturer. The study covered three cases, involving seven CS API, and considered the perspectives of syntactic analysis and syntactic-semantic analysis of API. Then, we conducted semi-structured interviews with five developers who worked on the SoIS to evaluate the tool’s efficiency. The results demonstrate that the tool is successful, with the syntactic and semantic similarity analysis of API proving to be efficient in identifying interoperability links between CS. From a practical point of view, the syntactic algorithms demonstrated notable performance in local environments. At the same time, semantic analysis, which is more robust, requires advanced infrastructure due to its high computational complexity. From a research perspective, the use of short text semantic similarity (STSS) is still a promising field, and the use of artificial intelligence with Large Language Models (LLM) can generate new results and discoveries of interoperability links in SoIS.