Inferring Models With Rule-Based Expert Systems (SoICT'14)

Many works related to software engineering rely upon formal models, e.g., to perform model-checking or automatic test case generation. Nonetheless, producing such models is usually tedious and error-prone. Model inference is a research field helping in producing models by generating partial models from documentation or execution traces (observed action sequences). This paper presents a new model generation method combining model inference and expert systems. It appears that an engineer is able to recognize the functional behaviors of an application from its traces by applying deduction rules. We propose a framework, applied to Web applications, simulating this reasoning mechanism, with inference rules organized into layers. Each yields partial IOSTSs (Input Output Symbolic Transition Systems), which become more and more abstract and understandable.

Online slides: http://slides.williamdurand.fr/soict14/
Sources: https://github.com/willdurand-slides/soict14

Transcript

1. Inferring Models With
   Rule-Based Expert Systems
   William Durand, Sébastien Salva — December 5, 2014 / SoICT'14
   

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3. Background @ Michelin
   

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4. 100+ applications running in production
   Not (fully) covered by tests
   Documentation most likely outdated
   MUST be maintained for ~20 years!
   

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5. The only way to ensure stability while fixing a bug
   is to learn how the application behaves.
   

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6. A first classic solution to express these behaviours is to
   model
   them, e.g. with
   I
   nput
   O
   utput
   S
   ymbolic
   T
   ransition
   S
   ystems.
   But writing such models is an
   heavy task
   and
   error prone
   .
   vNot suitable for Michelin applications.
   

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7. Hence the need for a
   model inference approach
   .
   v If models don't exist (or are too
   complicated to write), let's build them!
   

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8. Model Inference
   

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9. Inference is the act or process of reaching a conclusion
   about something from known facts or evidence.
   

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10. A (human) domain expert can deduce the meaning of an
    application execution by reading its traces.
    ì What about doing the same, programmatically?
    

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11. An expert system is a computer system that
    emulates the decision-making ability of a human expert.
    

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12. Model Inference Framework
    

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13. Model Generator Layers
    

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14. Hypotheses On The Rules
    A rule can only be applied a limited number of times on the
    same knowledge base
    The inference rules are Modus Ponens (soundness)
    Facts are Horn clauses
    Modus Ponens + Horn clauses Ű completeness
    

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15. Proof Of Concept
    Working with Web Applications
    

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16. Layer 1: Trace Filtering
    1. Cleaning-up the trace set given as input, removing noise
    2. Transforming "raw" traces into structured traces
    p HTTP requests and responses related to assets
    (CSS files, JavaScript files, images) are meaningless.
    

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17. Structured HTTP Traces
    Let t = req1, resp1, ..., reqn, respn be a raw
    HTTP trace composed of an alternate sequence of HTTP request
    reqi and HTTP response respi. The structured HTTP trace
    σ
    of t is the sequence (a1(p), θ1)...(an(p), θn) where:
    ai is the HTTP verb used to perform the request in reqi,
    p is the parameter set {URI, request, response},
    θi is a valuation p → Dp which assigns a value to each
    variables of p.
    θ
    is deduced from the values extracted from
    reqi and respi.
    The
    resulting trace set
    derived from raw traces is denoted ST.
    

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18. Example
    GET("https://github.com/", req0, resp0)
    GET("https://github.com/login", ...)
    POST("https://github.com/session", ...)
    GET("https://github.com/", ...)
    GET("https://github.com/willdurand", ...)
    GET("https://github.com/willdurand/Geocoder", ...)
    POST("https://github.com/logout", ...)
    GET("https://github.com/", ...)
    

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19. Layer 2: IOSTS Transformation
    1. Translating a structured trace set ST into a run set SR
    2. Deriving a first IOSTS tree δ1 from SR
    … The model is
    not
    extrapolated:
    traces(δ1) = ST
    

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20. Example
    

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21. Layers 3-N: IOSTS Abstraction
    Composed of rules that emulate the ability of a human expert
    to
    simplify transitions
    , to
    analyze transition syntax
    in order
    to deduce more meaningful information related to the
    targeted application, and to
    construct more abstract models
    .
    Each layer takes an
    IOSTS given by the direct lower one
    .
    It represents the current
    base of facts
    .
    

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22. Layer 3
    Layer 3 contains low level, generic rules that
    can be reused against different applications.
    rule "Identify Login Page"
    when
    $t: Transition(
    Action == GET,
    Guard.response.content contains('login-form')
    )
    then
    modify ($t) { Assign.add("isLoginPage := true") }
    end
    

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23. Layer 3 - Example
    

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24. Layer 4
    Layer 4 allows to infer a more abstract model
    composed of more expressive actions.
    rule "Identify Authentication"
    when
    $t1: Transition(Action == GET, Assign contains "isLoginPage:= true",
    $t1final := Lfinal)
    $t2: Transition(Action == PostRedirection, Linit == $t1lfinal,
    $t2linit := Linit)
    not Transition(Linit == $t2linit)
    then
    insert(new Transition("Authentication",
    Guard($t1.Guard,$t2.Guard), Assign($t1.Assign, $t2.Assign),
    $t1.Linit, $t2.Lfinal
    ))
    retract($t1)
    retract($t2)
    end
    

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25. Layer 4 - Example
    

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26. Need More?
    

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27. Conclusion
    

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28. Cons
    Writing (and maintaining) rules is harder than expected
    Preserving traces(M) = ST leads to rather large models
    

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29. Pros
    Original approach to derive IOSTS models
    It Works™
    

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30. Current Status
    Working on an implementation for Michelin's industrial systems.
    

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31. Future Work
    Generating test cases for regression testing.
    

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32. Thank You.
    Questions?
    

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