Paper Info
Title
Evolutionary testing of autonomous software agents
Abstract
A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents' developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically.
Year
DOI
Venue
2012
10.1007/s10458-011-9175-4
Autonomous Agents and Multi-Agent Systems
Keywords
Field
DocType
evolutionary optimisation,simulated autonomous agent,autonomous software agent,good test case,autonomous agent,overall approach,thorough testing,evolutionary testing,evolutionary algorithm,test case context,test case
Autonomous agent,Software engineering,Computer science,Evolutionary testing,Autonomy,Correctness,Software agent,Test case,Artificial intelligence,Distributed computing
Journal
Volume
Issue
ISSN
25
2
1387-2532
Citations 
PageRank 
References 
10
0.63
23
Authors
6
Name
Order
Citations
PageRank
Cu D. Nguyen122414.19
Simon Miles21599109.29
Anna Perini3116583.51
Paolo Tonella43559224.88
Mark Harman510264389.82
Michael Luck63440275.97