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

Autores
Rocha Araujo, Leonardo; Rodríguez, Guillermo Horacio; Vidal, Santiago; Marcos, Claudia A.; Santos, Rodrigo P.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
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
Materia
Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/151639

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spelling Empirical analysis on OpenAPI topic exploration and discovery to support the developer communityRocha Araujo, LeonardoRodríguez, Guillermo HoracioVidal, SantiagoMarcos, Claudia A.Santos, Rodrigo P.Ciencias InformáticasMicroservicesOpenAPIMigrationLegacy systemsTopic modelingOpenAPI 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 Operativa2022-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf68-69http://sedici.unlp.edu.ar/handle/10915/151639enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/301/250info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:11:05Zoai:sedici.unlp.edu.ar:10915/151639Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:11:05.727SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
title Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
spellingShingle Empirical analysis on OpenAPI topic exploration and discovery to support the developer community
Rocha Araujo, Leonardo
Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
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
dc.creator.none.fl_str_mv Rocha Araujo, Leonardo
Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
author Rocha Araujo, Leonardo
author_facet Rocha Araujo, Leonardo
Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
author_role author
author2 Rodríguez, Guillermo Horacio
Vidal, Santiago
Marcos, Claudia A.
Santos, Rodrigo P.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
topic Ciencias Informáticas
Microservices
OpenAPI
Migration
Legacy systems
Topic modeling
dc.description.none.fl_txt_mv 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
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.
publishDate 2022
dc.date.none.fl_str_mv 2022-10
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