Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain
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Presentation of the speech for the 42nd EUROMICRO Conference on Software Engineering and Advanced Applications 2016 held in Lymassol, Cyprus
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Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain
- 1. Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain Alessio Bucaioni, Antonio Cicchetti, Federico Ciccozzi, Saad Mubeen, Alfonso Pierantonio and Mikael Sjödin 31-08-2016 SEAA 2016 Arcticus Systems
- 2. 2 We discuss the use of modelling with uncertainty for representing the uncertainty that typically accompanies many stages of the software development of vehicular embedded systems. PRESENTATION TAKEAWAY
- 3. - BRAN SELIC Father of Real-Time UML “As our systems grow in complexity traditional code-centric development methods are becoming intractable” “More than 80 percent of vehicles’ innovation comes from embedded systems” - MANFRED BROY Professor of informatics at Technical University of Munich
- 4. BACKGROUND 4 Model-driven Engineering = Abstraction + Automation
- 6. BACKGROUND 6 Model Transformation automation Clock Connector data Connector trigger Data ports Trigger ports Timing constraints ts Proximity_Sensor_DFP Input_Process_DFP Path_Calculator_DFP CAN_Send_DFP nnector trigger Trigger ports Connector trigger Trigger ports ctor trigger Trigger ports
- 7. BACKGROUND 7 ONE-TO-MANY model transformations Design space generation/exploration Proximity_Sensor_DFP Input_Process_DFP Path_Calculator_DFP CAN_Send_DFP (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints
- 8. Analysis results Execution model Execution model MOTIVATION - Methodology 8 JTL transformation engine Functional model Execution models Timing analysis & filter Execution model Execution models Analysis results In-place model transformation Filtered execution models annotated with analysis results DesginlevelImplementationlevel EAST-ADL design model EAST-ADL design MM C2 Rubus model Rubus model Rubus models RubusMM C2 C2 Filtered RCM models annotated with analysis results Rubus model Rubus models
- 9. MOTIVATION – Motivating scenario: Intelligent ParkingAssist 9 Proximity_Sensor_DFP Input_Process_DFP Path_Calculator_DFP CAN_Send_DFP CAN_Receive_DFP Control_DFP Brake_Actuator_DFP IPAssistant_DFP Actuator_DFP TIMING REQUIREMENT: “ The calculated age and reaction delays shall not exceed 20 ms and 15 ms, respectevely ” 15 ms 20 ms
- 10. MOTIVATION – Motivating scenario: Intelligent ParkingAssist 10 (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints
- 11. MOTIVATION – Motivating scenario: Intelligent ParkingAssist 11 Timing analysis has filtered the solution space. However there are still 14 RCM models to inspect. (1) (2) (3) Software Circuit Clock Connector trigger Trigger ports
- 12. PROBLEM FORMULATION 12 How can we ease the inspection of the Rubus models solution space for applying domain knowledge which can not be automated?
- 13. CONTRIBUTION 13 Enhancing our methodology by introducing the u- RubusMM, a compact notation for representing the whole solution space by means of a single u-rubus model with uncertainty.
- 14. CONTRIBUTION 14 Analysis results u-JTL Timing analysis & filter Analysis results In-place model transformation DesginlevelImplementationlevel EAST-ADL design model EAST-ADL design MM C2 u-Rubus model RubusMM C2 C2 u-Rubus model annotated with analysis results u-JTL Rubus model Rubus models u-RubusMM C2
- 15. u-RubusMM 15 We endow Rubus with uncertainty elements. Romina Eramo, Alfonso Pierantonio, and Gianni Rosa. Managing uncertainty in bidirectional model transformations. In Proceedings of the 2015 ACM SIGPLAN International Conference on Software Language Engineering. Performed by an automated transformation, as follows: I. any class in Rubus is added to u-Rubus; in addition II. auxiliary classes Uclass and Iclass , with class and Uclass subclasses of Iclass , are added to u -Rubus; III. association uVariants : Uclass class is added to u -Rubus; IV. for each association a : class1 class2 in Rubus, an association a : class1 Iclass2 is added to u -Rubus.
- 16. Source Pattern Target Pattern #1 #2 u-RubusMM 16
- 18. u-Rubus (1) (2) (3) (4) Software Circuit Clock Connector data Connector trigger Data ports Trigger ports Timing constraints Timing constraints
- 19. u-Rubus
- 20. DISCUSSION 20 Q: The exploration of the solution space remains manual. A: This contribution represents a first step towards an analysis-based and semi-automatic designspace exploration mechanism. We are investigating the possibility to run timing analysis on a u-Rubus model. Q: The methodology only considers timing aspects. A: The idea of a compact notation can be exploited for successively exploring the solution space in relation to various properties. Exploration chain Q: The methodology generates too many solutions. A: The inability of real-time software to meet its primary non- functional requirements becomes apparent only in the later stages of development. When this happens, the design may have to be heavily and hurriedly modified. [Bran Selic]
- 21. CONCLUSIONAND FUTURE WORK 21 We have enhanced our previous methodology by introducing u-Rubus. Analysis results u-JTL Timing analysis & filter Analysis results In-place model transformation DesginlevelImplementationlevel EAST-ADL design model EAST-ADL design MM C2 u-Rubus model RubusMM C2 C2 u-Rubus model annotated with analysis results u-JTL Rubus model Rubus models u-RubusMM C2
- 22. Thank you for the attention! Questions?
Editor's Notes
- The software engineering community has agreed on three instruments when dealing with the increasing complexity of software and its development
- Model transformations can relieve developers from significant engineering effort and mitigate errors typical of manual translations. They can also potentially create an overwhelming amount of information.
- Design-space exploration techniques, characterised by one-to-many model transformations, have the potential to generate hundreds, thousands, or more, models. Despite we can filter the solution space, their usefulness for the engineer can be limited as the solution space is never really unveiled in the process. In fact, the engineer still has multiple choices and remains uncertain about the one to take: a decision can only be made by manually inspecting and comparing all candidate models.
- For the sake of simplicity, we consider only a portion of the software architecture consisting of two nodes connected to a single network that implements the Controller Area Network (CAN). The following timing requirement is specified too:
- According to our original methodology (Fig. 1), the EAST-ADL model in Fig. 2 is transformed in 32 Rubus models
- However, with the current support, such an inspection might be a daunting task as the selected Rubus models greatly overlap one with another
- The U-metaclasses represents uncertainty points where to anchor multiple alternative instances. Whereas, the role of the I-classes is to let the propagated interface composition in u -Rubus to refer to either a single Interface instance or to multiple instances through the UInterface (as subclass of IInterface ).