Faster Multi-Goal Simulation-Based Testing Using DoLesS (Domination with Least Square Approximation)
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For cyber-physical systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. For example, Arrieta et al. reported that state-of-the-art optimizers struggle to find minimal test suites for this task. To better manage this task, we propose DoLesS (Domination with Least Squares Approximation) which uses a domination predicate to sort the space of possible goals to a small number of representative examples. Multi-objective domination then divides these examples into a "best" set and the remaining "rest" set. After that, DoLesS applies an inverted least squares approximation approach to learn a minimal set of tests that can distinguish best from rest in the reduced example space. DoLesS has been tested on four cyber-physical models: a tank flow model; a model of electric car windows; a safety feature of an AC engine; and a continuous PID controller combined with a discrete state machine. Comparing to the recent state-of-the-art paper attempted the same task, DoLesS performs as well or even better as state-of-the-art, while running 80-360 times faster on average (seconds instead of hours). Hence, we recommend DoLesSas a fast method to find minimal test suites for multi-goal cyber-physical systems. For replication purposes, all our code is on-line:https://github.com/hellonull123/Test_Selection_2021.
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