An Adaptive Testing Approach Based on Field Data

TL;DR

This work tackles the challenge of testing evolving, safety-critical BSN systems post-deployment by introducing AdapTA, an ex-vivo Self-Adaptive Testing in the Field approach. AdapTA uses field data to build DTMC-based patient profiles, simulates patient behavior, and employs a MAPE-K loop to adapt test cases, the oracle, and test strategy in response to runtime changes. Experimental results across three adaptation scenarios show that AdapTA yields significantly higher failure-detection effectiveness (PTCR) than non-adaptive baselines, with scenario 3 showing the strongest improvements in critical-condition testing. The approach enables data-driven, adaptable validation of BSNs and offers a framework extendable to other evolving cyber-physical systems where field dynamics drive testing needs.

Abstract

The growing need to test systems post-release has led to extending testing activities into production environments, where uncertainty and dynamic conditions pose significant challenges. Field testing approaches, especially Self-Adaptive Testing in the Field (SATF), face hurdles like managing unpredictability, minimizing system overhead, and reducing human intervention, among others. Despite its importance, SATF remains underexplored in the literature. This work introduces AdapTA (Adaptive Testing Approach), a novel SATF strategy tailored for testing Body Sensor Networks (BSNs). BSNs are networks of wearable or implantable sensors designed to monitor physiological and environmental data. AdapTA employs an ex-vivo approach, using real-world data collected from the field to simulate patient behavior in in-house experiments.

Figures (1)

• Figure 1: Adaptive Testing Approach (AdapTA).