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MODELS 2020
Fri 16 - Fri 23 October 2020
Fri 23 Oct 2020 15:40 - 16:00 at Room A - A7-Safety, Security and Testing Chair(s): Joanne M. Atlee

Solving scheduling problems is important for a wide range of application domains including home care in the health care domain, allocation engineering in the automotive domain, and virtual network embedding in the network virtualisation domain. Standard solution approaches assume that an initially given problem definition (e.g. a set of constraints and an objective function) can be fixed, and does not have to be constantly changed and validated by domain experts. In this paper, we investigate an application where this is not the case: at dSPACE GmbH, a developer of software and hardware for mechatronic control systems, recurring manual tests must be executed in every development and release cycle. To allocate human resources (developers and testers) to perform these tests, a test schedule must be created and maintained during the testing process. Prior to our work, test scheduling at dSPACE was performed manually by a test manager, requiring more than one working day to create the initial schedule, and several hours of tedious, error-prone work every week to maintain the schedule. The novel challenge here is that an acceptable automation must be highly configurable by the test manager (the domain expert), who should be able to easily adapt and validate the problem definition on a regular basis. We demonstrate that techniques and results from consistency maintenance via triple graph grammars, and constraint solving via linear programming can be synergetically combined to yield a highly configurable and fully automated approach to test schedule generation. We evaluate our solution at dSPACE and show that it not only reduces the effort required to create and maintain schedules of acceptable quality, but that it can also be understood, configured, and validated by the test manager.

Fri 23 Oct
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