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Modelling and Simulation in Engineering
Volume 2016, Article ID 5309348, 15 pages
Research Article

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

1Leidos Inc., Huntsville, AL 35806, USA
2University of Alabama in Huntsville, Huntsville, AL 35899, USA

Received 28 April 2016; Revised 4 July 2016; Accepted 10 July 2016

Academic Editor: Min-Chie Chiu

Copyright © 2016 Kevin M. Betts and Mikel D. Petty. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.