Empirical analysis on OpenAPI topic exploration and discovery to support the developer community

OpenAPI has become a dominant standard for documentation in the service-oriented software industry. OpenAPI is used in many analysis and reengineering approaches for RESTful service and microservice-based systems. An OpenAPI document has several components that are usually filled by humans using nat...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Rocha Araujo, Leonardo, Rodríguez, Guillermo Horacio, Vidal, Santiago, Marcos, Claudia A., Santos, Rodrigo P.
Formato: Objeto de conferencia Resumen
Lenguaje:Inglés
Publicado: 2022
Materias:
Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/151639
https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/301/250
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-151639
record_format dspace
spelling I19-R120-10915-1516392023-05-03T20:04:19Z http://sedici.unlp.edu.ar/handle/10915/151639 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/301/250 issn:2451-7496 Empirical analysis on OpenAPI topic exploration and discovery to support the developer community Rocha Araujo, Leonardo Rodríguez, Guillermo Horacio Vidal, Santiago Marcos, Claudia A. Santos, Rodrigo P. 2022-10 2022 2023-04-18T15:16:38Z en Ciencias Informáticas Microservices OpenAPI Migration Legacy systems Topic modeling OpenAPI has become a dominant standard for documentation in the service-oriented software industry. OpenAPI is used in many analysis and reengineering approaches for RESTful service and microservice-based systems. An OpenAPI document has several components that are usually filled by humans using natural language (e.g. description of a certain functionality). Thus, subjectivity may lead to inconsistencies and ambiguities. Understanding what an API does is a challenging question. As a consequence, this issue could hinder developers from identifying the functionality of APIs, after reading all its components. Along this line, we argue that developers will be provided with supportive tools to find those APIs that better suit their needs. In this paper, we propose a step towards creating these kinds of tools by empirically analyzing a set of 2,000 OpenAPI documents with the goal of extracting the main topics of an API using three topic modeling algorithms. To address this issue, we focus on three tasks: i) determine which component of an OpenAPI document provides the most meaningful information, ii) compare three state-of-the-art topic modeling algorithms, and iii) determine the optimal number of topics to represent an API. Our findings show that the best results could be obtained from the Description component by using the Non-negative Matrix Factorization (NMF) or Latent Semantic Indexing (LSI) algorithms. To help developers find services in the OpenAPI directory, we also propose a prototype tool to explore the OpenAPI documents and analyze extracted topics to assess if the APIs meet developer’s needs. Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Resumen http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 68-69
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
spellingShingle Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
Rocha Araujo, Leonardo
Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
topic_facet Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
description OpenAPI has become a dominant standard for documentation in the service-oriented software industry. OpenAPI is used in many analysis and reengineering approaches for RESTful service and microservice-based systems. An OpenAPI document has several components that are usually filled by humans using natural language (e.g. description of a certain functionality). Thus, subjectivity may lead to inconsistencies and ambiguities. Understanding what an API does is a challenging question. As a consequence, this issue could hinder developers from identifying the functionality of APIs, after reading all its components. Along this line, we argue that developers will be provided with supportive tools to find those APIs that better suit their needs. In this paper, we propose a step towards creating these kinds of tools by empirically analyzing a set of 2,000 OpenAPI documents with the goal of extracting the main topics of an API using three topic modeling algorithms. To address this issue, we focus on three tasks: i) determine which component of an OpenAPI document provides the most meaningful information, ii) compare three state-of-the-art topic modeling algorithms, and iii) determine the optimal number of topics to represent an API. Our findings show that the best results could be obtained from the Description component by using the Non-negative Matrix Factorization (NMF) or Latent Semantic Indexing (LSI) algorithms. To help developers find services in the OpenAPI directory, we also propose a prototype tool to explore the OpenAPI documents and analyze extracted topics to assess if the APIs meet developer’s needs.
format Objeto de conferencia
Resumen
author Rocha Araujo, Leonardo
Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
author_facet Rocha Araujo, Leonardo
Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
author_sort Rocha Araujo, Leonardo
title Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title_short Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title_full Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title_fullStr Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title_full_unstemmed Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title_sort empirical analysis on openapi topic exploration and discovery to support the developer community
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/151639
https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/301/250
work_keys_str_mv AT rochaaraujoleonardo empiricalanalysisonopenapitopicexplorationanddiscoverytosupportthedevelopercommunity
AT rodriguezguillermohoracio empiricalanalysisonopenapitopicexplorationanddiscoverytosupportthedevelopercommunity
AT vidalsantiago empiricalanalysisonopenapitopicexplorationanddiscoverytosupportthedevelopercommunity
AT marcosclaudiaa empiricalanalysisonopenapitopicexplorationanddiscoverytosupportthedevelopercommunity
AT santosrodrigop empiricalanalysisonopenapitopicexplorationanddiscoverytosupportthedevelopercommunity
_version_ 1765659994367197184

Ejemplares similares