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Reducing Workload in Using AI-based API REST Test Generation

Published: 10 June 2024 Publication History

Abstract

Modern software applications, notably those utilizing microservices architectures, rely heavily on REST API technology for communication. Testing these APIs is challenging, time-consuming, and prone to errors. This paper introduces Pulse-UI, an AI-supported tool designed to enhance test sequence generation for REST APIs, aiming to reduce the workload involved in managing test sequences efficiently and improve overall test quality.

References

[1]
Ovidiu Banias, et al. "Automated Specification-Based Testing of REST APIs". In: Sensors 21.16 (2021). issn: 1424-8220.
[2]
Myeongsoo Kim et al. "Automated Test Generation for REST APIs: No Time to Rest Yet". In: Proceedings of the 31st International Symposium on Software Testing and Analysis. ISSTA 2022. Virtual, South Korea, 2022, pp. 289--301.
[3]
Testifi Pulse. 2023. url: https://testifi.io/pulse-api-testing/(visited on 11/16/2023).
[4]
Emanuele Viglianisi, Michael Dallago, and Mariano Ceccato. "RESTTESTGEN: Automated Black-Box Testing of RESTful APIs". In: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). 2020, pp. 142--152.

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Published In

cover image ACM Conferences
AST '24: Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)
April 2024
235 pages
ISBN:9798400705885
DOI:10.1145/3644032
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2024

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Author Tags

  1. software testing
  2. AI-based testing
  3. REST API
  4. automation
  5. teaching AI system
  6. test case scenarios
  7. human support

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