What Not to Test (for Cyber-Physical Systems)

TL;DR

This paper introduces DoLesS, a method that efficiently selects minimal, effective test cases for simulation-based systems by balancing multiple optimization goals, significantly reducing computation time while maintaining or improving test effectiveness.

Contribution

It proposes DoLesS, a novel approach using inverted least squares approximation to optimize test case selection based on multiple goals, outperforming prior methods in speed and effectiveness.

Findings

DoLesS achieves comparable or better test effectiveness than state-of-the-art methods.

It reduces test selection time by 80-360 times, from hours to seconds.

The method is effective across multiple simulation-based systems.

Abstract

For simulation-based systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. Domain-specific optimization goals (e.g. maximize output variance) are useful for guiding the rapid selection of test cases via mutation. But evaluating the selected test cases via mutation (that can distinguish the current program from the mutated systems) is a different goal to domain-specific optimizations. While the optimization goals can be used to guide the mutation analysis, that guidance should be viewed as a weak indicator since it can hurt the mutation effectiveness goals by focusing too much on the optimization goals. Based on the above, this paper proposes DoLesS (Domination with Least Squares Approximation) that selects the minimal and effective test cases by averaging over a coarse-grained grid of the information gained from multiple optimizations goals. DoLesS applies an inverted least squares approximation approach to find a minimal set of tests that can distinguish better from worse parts of the optimization goals. When tested on multiple simulation-based systems, DoLesS performs as well or even better as the prior state-of-the-art, while running 80-360 times faster on average (seconds instead of hours).