Targeting the Right Situations: Mutation Testing of Fuzzy-Powered Situation Hyperspace

Abstract

Ensuring the safety of autonomous Unmanned Aerial Vehicles (UAVs) operating in underground mines is challenging due to the vast and uncertain Operational Design Domain (ODD). Traditional validation approaches, such as random testing, often result in inefficient situation coverage, allocating computational resources to low-risk scenarios while failing to expose critical edge cases. Building on prior work in risk-prioritized Situation Coverage, this paper investigates whether Fuzzy Logic-based risk estimation effectively correlates with real system failures. To this end, we conduct a mutation testing study within our SCALOFT simulation framework. A fixed set of software mutants is systematically injected and evaluated under two testing strategies: (i) uniformly random situation generation and (ii) Fuzzy-Guided risk-prioritized situation generation. Experimental results show that the Fuzzy-Guided approach achieves a higher mutation score and is more effective at exposing difficult-to-detect faults compared to random testing.