@inproceedings{veanes2006online,
author = {Veanes, Margus and Roy, Pritam and Campbell, Colin},
title = {Online Testing with Reinforcement Learning},
series = {LNCS},
booktitle = {Formal Approaches to Software Testing and Runtime Verification, FATES/RV 2006},
year = {2006},
month = {January},
abstract = {Online testing is a practical technique where test derivation and test execution are combined into a single algorithm. In this paper we describe a new online testing algorithm that optimizes the choice of test actions using Reinforcement Learning (RL) techniques. This provides an advantage in covering system behaviors in less time than with a purely random choice of test actions. Online testing with conformance checking is modeled as a 1.5 player game, or Markov Decision Process (MDP), between the tester as one player and the implementation under test (IUT) as the opponent. Our approach has been implemented in C#, and benchmark results are presented in the paper. The specifications that generate the tests are written as model programs in any .NET language such as C# or VB.},
publisher = {Springer Verlag},
url = {http://approjects.co.za/?big=en-us/research/publication/online-testing-with-reinforcement-learning/},
pages = {240-253},
volume = {4262},
edition = {Formal Approaches to Software Testing and Runtime Verification, FATES/RV 2006},
}