Paper Info
Title
AI-driven web API testing
Abstract
ABSTRACTTesting 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 internally (services relying on that API) and externally (third-party applications and end users). Most existing tools and testing approaches 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") capable 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.
Year
DOI
Venue
2020
10.1145/3377812.3381388
International Conference on Software Engineering
Keywords
DocType
ISSN
Web API, RESTful API, testing framework, automated software testing, artificial intelligence
Conference
0270-5257
ISBN
Citations 
PageRank 
978-1-7281-6528-8
1
0.36
References 
Authors
11
1
Name
Order
Citations
PageRank
Alberto Martin-Lopez193.48