Industrial Challenge

For the third time, the WATERS workshop will discuss this year design and verification challenges proposed by industrial partners.

The purpose of the WATERS industrial challenge is to share ideas, experiences and solutions to concrete timing design and verification problems issued from real industrial case studies. We aim at promoting discussions, closer interactions and cross fertilization of ideas within the real-time research community but also together with industrial practitioners from different application domains.

The 2017 edition will feature:

  • solutions to the 2017 challenge proposed by Bosch, which extends the 2016 challenge: The challenge consists in providing concepts for realizing implicit and LET communication, then assessing their impact on the end-to-end latency along a set of given cause-effect chains in a full blown engine management software.
  • an initial version of the 2018 industrial challenge proposed by Dassault: The use case is based on a small drone-like cyber-physical system. The challenge consists in contributing the concepts, models, candidate technologies, and analyses of the proposed use case's middleware layer. Contributions are expected on timing-contract languages, suitable abstractions of the concrete technological solutions, compositionality and genericity of the correctness arguments.

More information on the WATERS website: https://waters2017.inria.fr/challenge

Previous editions of the challenge:

  • The 2016 challenge, proposed by Bosch, consisted in determining tight end-to-end latency bounds for a set of given cause-effect chains in a full blown engine management software.
  • The 2015 challenge, proposed by Thales, consisted of an aerial video tracking system used in intelligence, surveillance, reconnaissance, tactical and security applications. The system is characterized by strict and less strict constraints on timing. Two timing verification problems are proposed to the community, which consist in calculating various timing latencies and optimizing priority assignments.