Universidade Federal do Ceará
Mestrado e Doutorado em Ciências da Computação

Título: A Syntactic-Semantic Analysis Method for Automatic API Connection Points Discovery in Systems-of-Information Systems

Data: 05/12/2024
Horário: 15h
Local: Google Meet (https://meet.google.com/apr-mtbw-icu)

Resumo:

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. In order to contribute to this 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, where we outline the motivations and justifications for creating the method; (ii) design, where we detail all the phases and components necessary to obtain the API connection points; and (iii) 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 performance of the tool 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 I, which covers two industrial cases scenarios with four real CS API from a SoIS of a global computer manufacturer. Then, we conducted semi-structured interviews with five experienced developers working within the target SoIS to evaluate our tool’s effectiveness. The results demonstrate that our tool performs successfully across different environments, with API syntactic similarity analysis proving efficient in identifying CS interoperability links. From a research perspective, the combination of syntactic and semantic analyses shows promise for delivering more accurate and reliable results. To further enhance this, we propose incorporating a semantic similarity layer into our method, leveraging deep learning algorithms based on natural language processing. That addition aims to improve the precision in identifying API connection points, thereby increasing the tool’s overall effectiveness. Furthermore, we will select additional real-world CS cases in SoIS and perform a comprehensive analysis of the outcomes.

Banca examinadora:

• Prof. Dr. Lincoln Souza Rocha (MDCC/UFC) – Orientador
• Prof. Dr. Paulo Henrique Mendes Maia (UECE) – Coorientador
• Prof. Dr. Paulo Antonio Leal Rêgo (MDCC/UFC)
• Prof. Dr. João Paulo Pordeus Gomes (UFC)
• Prof. Dr. Rodrigo Pereira dos Santos (UNIRIO)