Autofunk: An Inference-Based Formal Model Generation Framework for Production Systems (FM 2015)

In this paper, we present Autofunk, a fast and scalable framework designed at Michelin to automatically build formal models (Symbolic Transition Systems) based on production messages gathered from production systems themselves. Our approach combines model-driven engineering with rule-based expert systems and human knowledge.

Online slides: http://slides.williamdurand.fr/fm2015/
Sources: https://github.com/willdurand-slides/fm2015

Transcript

1. Autofunk: an Inference-Based
   Formal Model Generation
   Framework for [Michelin]
   Production Systems.
   William Durand, Sébastien Salva — June 25, 2015 / FM'15
   

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3. Quick Tour @ Michelin
   

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4. A factory is divided into several workshops,
   one for each step of the manufacturing process.
   

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5. A production system is composed of devices, production
   machines, and one or more software to control them.
    In our case, we target a single workshop only.
   

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6. Software exchange information with points and machines by
   sending and receiving production messages.
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7. Production messages are exchanged in a binary format (custom
   protocols), through centralized messaging systems.
   

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8. Each production message is tied to a product (e.g. tire),
   identified by a product identifier (p
   i
   d
   ).
   Gathering all production messages related to a product
   allows to retrieve what happened to it.
   

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9. Background
   

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10. Developement Teams POV
    100+ applications running in production
    Not (fully) covered by tests
    Documentation most likely outdated
    MUST be maintained for ~20 years!
    

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11. Customers (Factories) POV
    Stability over anything else
    Maintenance periods are planned,
    but rather long (> 1 week)
    1h (unexpected) downtime = 50k $
    

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12. Testing such production systems is complex,
    and takes a lot of time as it implies the physical
    devices, and there are numerous behaviours.
    

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13. These behaviours could be formally described into a model.
    But writing such models is an heavy task and error prone.
     Not suitable for Michelin applications.
    

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14. Our Approach
    By leveraging the information found in the production messages,
    we build formal and exact models (STS) that describe functional
    behaviours of a production system under analysis.
    

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15. The Big Picture
    

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16. In Depth Autofunk
    

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17. Autofunk
    Combines different fields: model inference,
    expert systems, and (now) machine learning
    Written in Java 8, reusing powerful libraries
    (e.g. , )
    More a Proof of Concept than a production-
    ready tool
    To be open sourced (no ETA yet)
    Spark Drools
    

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18. Experimentation
    10 million production messages (20 days)
    
    161,035 traces
    
    S R
    (
    S
    )
    77,058 branches 1,587 branches
    43,536 branches 1,585 branches
     2 entry points here
    It took 5 minutes to build the two models.
    

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19. Work In Progress
    

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20. Offline Passive Testing
    Inferred models are used as specifications
    Another set of traces is collected on a system
    under test S
    U
    T
    (new or upgraded)
    
    Does S
    U
    T
    conforms to the specifications?
    

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21. Conclusion
    Fast and efficient technique to infer formal models
    The more production messages, the better!
    But a few technical issues to tackle (memory
    consumption for instance)
    

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22. Future Work
    Deploying Autofunk as a real solution (WIP)
    Offline passive testing (WIP)
    Online passive testing
    

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23. Thank You.
    Questions?
    

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