Situation Coverage Testing for a Simulated Autonomous Car -- an Initial Case Study
This addresses the problem of testing diversity in autonomous robot software for developers, but it is incremental as it builds on prior proposals with limited empirical evidence.
The paper tackled testing autonomous robot software by implementing situation coverage for a simulated autonomous car and comparing it to random test generation in finding seeded faults. The result showed similar performance with only a slight advantage for situation coverage, concluding it may not be significant for simple software but could be valuable for more complex systems.
It is hard to test autonomous robot (AR) software because of the range and diversity of external situations (terrain, obstacles, humans, peer robots) that AR must deal with. Common measures of testing adequacy may not address this diversity. Explicit situation coverage has been proposed as a solution, but there has been little empirical study of its effectiveness. In this paper, we describe an implementation of situation coverage for testing a simple simulated autonomous road vehicle, and evaluate its ability to find seeded faults compared to a random test generation approach. In our experiments, the performance of the two methods is similar, with situation coverage having a very slight advantage. We conclude that situation coverage probably does not have a significant benefit over random generation for the type of simple, research-grade AR software used here. It will likely be valuable when applied to more complex and mature software.