Izenburua
Performance-Driven Metamorphic Testing of Cyber-Physical SystemsBeste instituzio
Universidad de SevillaOrona S.Coop.
Bertsioa
Postprinta
Eskubideak
© 2022 IEEESarbidea
Sarbide bahituaArgitaratzailearen bertsioa
https://dx.doi.org/10.1109/TR.2022.3193070Non argitaratua
IEEE Transactions on Reliability IEEE. August, 2022Argitaratzailea
IEEEGako-hitzak
Elevators
testing
Materials requirements planning
Control systems ... [+]
testing
Materials requirements planning
Control systems ... [+]
Elevators
testing
Materials requirements planning
Control systems
Computer bugs
Meteorology
Mathematical models [-]
testing
Materials requirements planning
Control systems
Computer bugs
Meteorology
Mathematical models [-]
Laburpena
Cyber-physical systems (CPSs) are a new generation of systems, which integrate software with physical processes. The increasing complexity of these systems, combined with the uncertainty in their inte ... [+]
Cyber-physical systems (CPSs) are a new generation of systems, which integrate software with physical processes. The increasing complexity of these systems, combined with the uncertainty in their interactions with the physical world, makes the definition of effective test oracles especially challenging, facing the well-known test oracle problem. Metamorphic testing has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of the system, so-called metamorphic relations (MRs). In this article, we propose an MR pattern called PV for the identification of performance-driven MRs, and we show its applicability in two CPSs from different domains, which are automated navigation systems and elevator control systems. For the evaluation, we assessed the effectiveness of this approach for detecting failures in an open-source simulation-based autonomous navigation system, as well as in an industrial case study from the elevation domain. We derive concrete MRs based on the PV pattern for both case studies, and we evaluate their effectiveness with seeded faults. Results show that the approach is effective at detecting over 88% of the seeded faults, while keeping the ratio of FPs at 4% or lower. [-]