RAISE2015.pdf (241.51 kB)
Black-Box Test Generation from Inferred Models
conference contribution
posted on 2015-05-07, 10:44 authored by Petros Papadopoulos, Neil WalkinshawAutomatically 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.
History
Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer ScienceSource
International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE'15), Florence, ItalyVersion
- AM (Accepted Manuscript)