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Black-Box Test Generation from Inferred Models

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conference contribution
posted on 07.05.2015, 10:44 by Petros Papadopoulos, Neil Walkinshaw
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.

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Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Source

International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE'15), Florence, Italy

Version

AM (Accepted Manuscript)

isbn

978-1-4799-1934-5

Copyright date

2015

Available date

25/08/2015

Temporal coverage: start date

17/05/2015

Temporal coverage: end date

17/05/2015

Language

en

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