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Semantic analysis of RESTful APIs for the detection of linguistic patterns and antipatterns
Concordia University, Canada.ORCID iD: 0000-0001-7092-2244
Blekinge Institute of Technology.
Université du Québec à Montréal, Canada.
Université du Québec à Montréal, Canada.
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2017 (English)In: International Journal of Cooperative Information Systems, ISSN 0218-8430, Vol. 26, no 2, p. 1-37, article id 1742001Article in journal (Refereed) Published
Abstract [en]

Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

Place, publisher, year, edition, pages
World Scientific, 2017. Vol. 26, no 2, p. 1-37, article id 1742001
Keywords [en]
RESTful APIs, Linguistic antipatterns, Patterns, Detection, Semantic analysis, Latent Dirichlet Allocation (LDA), Second-order similarity, Documentation
National Category
Computer Systems
Research subject
Computer Science, Software Technology; Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-80812DOI: 10.1142/S0218843017420011OAI: oai:DiVA.org:lnu-80812DiVA, id: diva2:1291602
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SOFAAvailable from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-03-26Bibliographically approved

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Palma, Francis

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf