Problem
Research questions and friction points this paper is trying to address.
synchronous dataflow
Lustre
teaching
formal specification
embedded systems
Teaching Synchronous Dataflow Modelling with Learn-Heptagon
🤖 AI Summary
This work addresses the disconnect between programming and formal verification in teaching the synchronous dataflow language Lustre by proposing and implementing Learn-Heptagon, an integrated online educational platform. The platform combines web-based Lustre editing, simulation, and model checking capabilities, enabling automatic verification of synchronous observers and assume-guarantee contracts. It is accompanied by a pedagogical approach that seamlessly integrates programming exercises with formal specification tasks. Deployed in engineering courses, Learn-Heptagon significantly lowers the learning barrier and effectively enhances students’ comprehension and practical skills in synchronous dataflow modeling and formal methods.
📝 Abstract
Lustre is a synchronous dataflow language designed to implement safety-critical embedded software. In addition to writing executable programs, the language doubles as a program logic, used for writing specification as synchronous observers or assume-guarantee contracts that specify properties of these programs. These specifications may be used during testing or proved exhaustively using model-checking tools. We taught a course on Lustre to last year engineering students. To streamline the learning experience and avoid technical issues, we developped an online application, Learn-Heptagon, which allows for writing, simulating, and proving properties of Lustre programs. This paper presents the application and the associated lesson plan.
Innovation
Methods, ideas, or system contributions that make the work stand out.
synchronous dataflow
Lustre
formal verification
online teaching platform
model checking
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