1 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and.

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1 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Ptolemy Hierarchical Orthogonal Multi-Attribute Solver (PtHOMAS) Ptolemy mini-conference April Jackie Mankit Leung, Thomas Huining Feng (UC Berkeley) Thomas Mandl, Elizabeth Latronico, Charles Shelton (Bosch)

2 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Motivation  Cars are networked software systems Up to 70 Electronic Control Units Software crucial for many features Electronic stability control Parking assist Emissions control Engine Start/Stop Active and passive safety  How can we manage increasing complexity and interconnectedness?  Analysis approaches promising, but hand-annotation has drawbacks Time intensive to develop and maintain People are inconsistent, make errors Repeat for every composition

3 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. How can a computer think like a software designer?  Constant / Non-constant example All sources are constant, so Display is constant (here, 21) If any source is non-constant, Display could be non-constant  How does a person know that?  How are constant and non-constant related?  What are the rules for each individual actor?  What rules govern the connections among actors? 21 ? ?

4  The ontology consists of two things: A property lattice, consisting of: Property (“Concept”) Edge (“Relationship”) Constraints on property-able objects (actors, expressions, …) Inequality terms Monotonic functions  Implementation Re-use Ptolemy type system constraint solving algorithms New! Graphical interface (for both lattice, inequality constraints) Java class definition for special cases PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Frame the problem with an ontology For example: P(output) >= P(input) Property Edge

5 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Property lattice example  Complete partial ordering of our properties Common bottom element Common top element Can promote higher in lattice, but not lower  Type system is a familiar example Integer to Double is OK 3 to 3.0 Double to Integer is not! 3.14 to 3 General Boolean Not a Type Integer Double Complex Scalar Long Simplified Ptolemy type system

6 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Constant / Non-constant lattice  How are constant and non-constant related? Define a new lattice: Constant: Value is always the same Non-constant: Value might change (but, perhaps does not) Correct but conservative In this case, since there are no branches, Non-constant is the common top element  Can define your own lattice for your properties! Constant Non-Constant Unknown

7 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Constraint analysis process  Ptolemy has a scalable and efficient solver algorithm Linear with the number of constraints Static analysis, so no execution required Accepts a list of pairwise inequalities Solves for least (what we want) or greatest upper bound  Constraint generation Need rules for each object in library; sharing possible Map objects to inequality terms (ports for most actors) Set of rules for finite state machines and expressions Constraint list a >= Constant b >= a c >= a Solver Result a, b, c == Constant Rule output >= input Constraint list Actor.out >= Actor.in1 Actor.out >= Actor.in2 Actor2.out >= Actor2.in1

8 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Property constraints for actors in example  Const actor: output >= Constant  Sequence actor: output >= Non-constant  Math actors (AddSubtract, MultiplyDivide, Maximum) output >= input, for all input signals output >= input1 output >= input2 Extends to multiple channels for multiport

9 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Model-wide property constraints for connections  Model-wide constraints are specified in new “solver box” actor  Here, sink >= source for connections Add1.input >= C1.output Add1.input >= C2.output Multiply3.input >= Add1.output Add1.input C2.output C1.output Add1.output Multiply3.input

10 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Results for the example  Property annotation at every point in large models with minimal user effort All constant sourcesOne non-constant source

11 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Future topics  Ontologies New lattices for a variety of properties and use cases Learn about existing ontologies from different communities What ontology concepts are expressible in a lattice?  Model transformation Use property annotations to guide and simplify model transformation  Property correctness and extraction Verify hand-written rules Automatically extract rules for new objects Relate to partial evaluation / program specialization  For a more interesting demo, see us at the break!  Thanks! 

12 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. Backup slides

13 PtHOMAS project, Ptolemy mini-conference '09 CR/RTC3-NA Elizabeth Latronico; UC Berkeley Jackie Mankit Leung | 4/16/2009 | © 2009 Robert Bosch LLC and affiliates. All rights reserved. package ptolemy.data.properties.lattice.logicalAND; public class Lattice extends PropertyLattice { public Property TRUE = new True(this); public Property FALSE = new False(this); public Property UNKNOWN = new Unknown(this); public Lattice() { addNodeWeight(TRUE); addNodeWeight(FALSE); addNodeWeight(UNKNOWN); addEdge(UNKNOWN, TRUE); addEdge(TRUE, FALSE); if (!isLattice()) { throw new Exception("The PropertyLattice is not a lattice."); } Lattice Java Class The package defines the use- case / ontology for this lattice. Properties are defined as public fields. Relationship is specified by adding edges from one element to another.