Putting Image Manipulations in Context: Robustness Testing for Safe Perception

2018 ◽  
Author(s):  
Zachary Pezzementi ◽  
Trenton Tabor ◽  
Samuel Yim ◽  
Jonathan K. Chang ◽  
Bill Drozd ◽  
...  
Keyword(s):  
Robustness Testing

Robustness testing of non-hierarchic system optimization software

1994 ◽  
Author(s):  
Ernest Eason ◽  
Gustav Nystrom ◽  
Aragon Burlingham ◽  
Edward Nelson
Keyword(s):  
System Optimization ◽  
Optimization Software ◽  
Robustness Testing ◽  
Hierarchic System

Simple and Efficient Solution for Robustness Testing in Gradient Elution Liquid Chromatographic Methods

2018 ◽  
Vol 81 (8) ◽  
pp. 1135-1145 ◽  
Author(s):  
Jasmina Šljivić ◽  
Ana Protić ◽  
Anđelija Malenović ◽  
Biljana Otašević ◽  
Mira Zečević
Keyword(s):  
Efficient Solution ◽  
Gradient Elution ◽  
Robustness Testing ◽  
Chromatographic Methods ◽  
Liquid Chromatographic

scholarly journals Automated Search-Based Robustness Testing for Autonomous Vehicle Software

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Kevin M. Betts ◽  
Mikel D. Petty
Keyword(s):  
Monte Carlo ◽  
Performance Metrics ◽  
Initial Conditions ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Test Case ◽  
Test Cases ◽  
Control Software ◽  
Robustness Testing ◽  
Testing Techniques

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.

Accelerated model-based robustness testing of state machine implementations

2013 ◽  
Vol 13 (3) ◽  
pp. 50-67 ◽  
Author(s):  
Patrick Heckeler ◽  
Hanno Eichelberger ◽  
Thomas Kropf ◽  
Jürgen Ruf ◽  
Stefan Huster ◽  
...  
Keyword(s):  
State Machine ◽  
Robustness Testing ◽  
Model Based
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