Testing the Untestable: Model Testing of Complex Software-Intensive Systems

Objective

Software-intensive systems pervade modern society and industry. These systems often play critical roles from an economic, safety or security standpoint, thus making their dependability indispensible. Software Verification and Validation (V&V) is core to ensuring software dependability. The most prevalent V&V technique is testing, that is the automated, systematic, and controlled execution of a system to detect faults or to show compliance with requirements. Increasingly, we are faced with systems that are untestable, meaning that traditional testing methods are highly expensive, time-consuming or infeasible to apply due to factors such as the systems’ continuous interactions with the environment and the deep intertwining of software with hardware.
TUNE will enable testing of untestable systems by revolutionising how we think about test automation. Our key idea is to frame testing on models rather than operational systems. We refer to such testing as model testing. The models that underlie model testing are executable representations of the relevant aspects of a system and its environment, alongside the risks of system failures. Such models inevitably have uncertainties due to complex, dynamic environment behaviours and the unknowns about the system. This necessitates that model testing be uncertainty-aware.
We propose to develop scalable, practical and uncertainty-aware techniques for test automation, leveraging our expertise on model-driven engineering and automated testing. Our solutions will synergistically combine metaheuristic search with system and risk models to drive the search for critical faults that entail the most risk. TUNE is the first initiative with the specific goal of raising the level of abstraction of testing from operational systems to models. The project will bring early and cost-effective automation to the testing of many critical systems that defy existing automation techniques, thus significantly improving the dependability of such systems.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• natural sciences computer and information sciences software
• engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
• natural sciences computer and information sciences data science natural language processing
• social sciences sociology industrial relations automation
• natural sciences computer and information sciences artificial intelligence machine learning

Host institution

Beneficiaries (1)