Ponencia
AI-driven web API testing
Autor/es | Martín López, Alberto |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2020 |
Fecha de depósito | 2022-05-24 |
Publicado en |
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ISBN/ISSN | 978-1-4503-7122-3 |
Resumen | Testing of web APIs is nowadays more critical than ever before,
as they are the current standard for software integration. A bug
in an organization’s web API could have a huge impact both in ternally (services relying ... Testing of web APIs is nowadays more critical than ever before, as they are the current standard for software integration. A bug in an organization’s web API could have a huge impact both in ternally (services relying on that API) and externally (third-party applications and end users). Most existing tools and testing ap proaches require writing tests or instrumenting the system under test (SUT). The main aim of this dissertation is to take web API testing to an unprecedented level of automation and thoroughness. To this end, we plan to apply artificial intelligence (AI) techniques for the autonomous detection of software failures. Specifically, the idea is to develop intelligent programs (we call them “bots”) ca pable of generating hundreds, thousands or even millions of test inputs and to evaluate whether the test outputs are correct based on: 1) patterns learned from previous executions of the SUT; and 2) knowledge gained from analyzing thousands of similar programs. Evaluation results of our initial prototype are promising, with bugs being automatically detected in some real-world APIs. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España Ministerio de Ciencia, Innovación y Universidades (MICINN). España Ministerio de Educación, Cultura y Deporte (MECD). España |
Identificador del proyecto | BELI (TIN2015-70560-R)
RTI2018-101204-B-C21 (HORATIO) FPU17/04077 |
Cita | Martín López, A. (2020). AI-driven web API testing. En ICSE 2020: ACM/IEEE 42nd International Conference on Software Engineering: Companion (202-205), Seoul, South Korea: Association for Computing Machinery (ACM). |
Ficheros | Tamaño | Formato | Ver | Descripción |
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AI-Driven Web API Testing.pdf | 940.5Kb | [PDF] | Ver/ | |