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Title: Black-Box Test Generation from Inferred Models
Authors: Papadopoulos, Petros
Walkinshaw, Neil
First Published: 17-May-2015
Presented at: International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE'15), Florence, Italy
Start Date: 17-May-2015
End Date: 17-May-2015
Abstract: Automatically generating test inputs for components without source code (are ‘black-box’) and specification is challenging. One particularly interesting solution to this problem is to use Machine Learning algorithms to infer testable models from program executions in an iterative cycle. Although the idea has been around for over 30 years, there is little empirical information to inform the choice of suitable learning algorithms, or to show how good the resulting test sets are. This paper presents an openly available framework to facilitate experimentation in this area, and provides a proof-of-concept inference-driven testing framework, along with evidence of the efficacy of its test sets on three programs.
ISBN: 978-1-4799-1934-5
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2015 IEEE. by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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