2 Introduction to MBSE

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The University of Sydney Page 1 Week 2: Introduction to MBSE Presented by Dong YUAN School of Electrical and Information Engineering dong.yuan@sydney.edu.au
The University of Sydney Page 2 Content – Project Theme – Introduction to Model Based Software Engineering
The University of Sydney Page 3 About the recent development in AI AI Machine Learning Neural Networks () Deep Learning (DNN) LLM
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The University of Sydney Page 4 Large Language Models -> AGI – Development of IT in recent 10 years – Cloud -> Big Data -> AI/5G -> IoT/Edge Computing applications – XaaS becomes the main way for delivering/using applications – AGI refers to highly autonomous systems that outperform humans at most economically valuable work. – AGI possesses human-like cognitive abilities, as opposed to specialized tasks of Narrow AI. – AGI could revolutionize industries, science, and society.
The University of Sydney Page 5 Applications of LLMs – Natural Language Understanding – Language Translation – Named Entity Recognition: Identifying names, places, and more. – Sentiment Analysis: LLMs gauge emotions from text data. – Content Generation – LLMs generate articles, stories, and essays. – Crafting engaging and relevant social content. – Automated writing for e-commerce platforms. – Chatbots and Virtual Assistants – LLMs provide instant, personalized responses, and answering queries from vast text sources. – Avatar, Natural interactions with AI-driven systems.
The University of Sydney Page 6 Applications of LLMs (cont.) – Creative Writing and Art – LLMs compose poetry in various styles. – Crafting narratives, plots, characters, dialogues. – Educational Aids – Learning Resources: LLMs generate customized educational content. – Summarizing complex topics for students. – Language Learning: Contextual language exercises and examples. – Accessibility – Text-to-Speech: Converting written text into spoken words. – Audiobook Production: LLMs narrate books for the visually impaired. – LLMs as transformative tools in communication and understanding. – Etc.
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The University of Sydney Page 7 Issues of Ethic, Safety, Security, etc. – Ensuring responsible use to prevent misinformation – Handling sensitive data and personal information – Preventing accidental leaks in generated text – Masking sensitive information – Ensuring LLM applications are fair and unbiased – Etc.
The University of Sydney Page 8 Content – Project Theme – Introduction to Model Based Software Engineering
The University of Sydney Page 10 Abstraction => Model Thing Model of Thing abstraction – Abstraction is forgetting (some) details
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The University of Sydney Page 11 Level of Abstraction Problem Domain Machine HLL Problem Domain Machine HLL Model Abstraction using Model "A programming language is low level when its programs require attention to the irrelevant.” - Alan J. Perlis
The University of Sydney Page 12 What is a Model?
The University of Sydney Page 13 What is a Model? A Model… – is a representation of a part of function, structure, behaviour of a system – is a simplification of a system – information, abstraction, specification – can answer question in place of actual system – inference, prediction – is used for a purpose – planning, design, risk analysis, testing
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The University of Sydney Page 14 Overview MBSE – The Problem – The Premise – The Results
The University of Sydney Page 15 The Enemy: Complexity – Modern software systems are reaching levels of complexity comparable to those of biological systems System of system – It causes problems and errors in system planning, architectural design and development. Projects fail, miss their deadlines or exceed their budgets. – Building a complex system is always connected to a risk of mistakes and missed requirements. Humans are intrinsically prone to errors when handling too many details.
The University of Sydney Page 16 Fred Brooks on Complexity – [From: F. Brooks, “The Mythical Man - Month”, Addison Wesley, 1995] – Essential complexity – inherent to the problem – cannot be eliminated by technology or technique – simple solutions would not adequately solve the problem – Accidental complexity – due to implementation technology or methods used to solve the problem – ineffective planning or project management, a mismatching or immature implementation technology
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The University of Sydney Page 17 Accidental Complexity can be avoidable – One of the main tasks in software engineering is to discover means to minimize accidental complexity. – Raising the level of abstraction of the technology used for software development is one of the basic means for guarding against accidental complexity. – Margaret Hamilton – Mother of software engineering
The University of Sydney Page 18 SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start void generate_data () { for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} } SC_CTOR(producer) { SC_METHOD(generate_data); sensitive << start;}}; SC_MODULE(consumer) { sc_inslave<int> in1; int sum; // state variable void accumulate (){ sum += in1; cout << “Sum = “ << sum << endl;} SC_CTOR(consumer) { SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container { producer *A1; consumer *B1; sc_link_mp<int> link1; SC_CTOR(top) { A1 = new producer(“A1”); A1.out1(link1); B1 = new consumer(“B1”); B1.in1(link1);}}; Can you see what this program is about? Back to “Modern” Software
The University of Sydney Page 19 …Corresponding UML Model « sc_slave » b1 :Consumer « sc_method » a1: Producer start out1 in1 Can you see it now?
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The University of Sydney Page 20 SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start void generate_data () { for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} } SC_CTOR(producer) { SC_METHOD(generate_data); sensitive << start;}}; SC_MODULE(consumer) { sc_inslave<int> in1; int sum; // state variable void accumulate (){ sum += in1; cout << “Sum = “ << sum << endl;} SC_CTOR(consumer) { SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container { producer *A1; consumer *B1; sc_link_mp<int> link1; SC_CTOR(top) { A1 = new producer(“A1”); A1.out1(link1); B1 = new consumer(“B1”); B1.in1(link1);}}; The Program and Its Model « sc_slave » b1 :Consumer « sc_method » a1: Producer start out1 in1 «sc_link_mp» link1
The University of Sydney Page 21 Overview – The Problem – The Premise – The Results
The University of Sydney Page 22 Why Do Engineers Build Models? – To understand – …the interesting characteristics of an existing or desired (complex) system and its environment – To predict – …the interesting characteristics of the system by analysing its model(s) – To communicate – …their understanding and design intent (to others and to oneself!) – To specify – ...the implementation of the system (models as blueprints)
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The University of Sydney Page 23 Engineering Models – Engineering model: – A selective representation of some system that captures accurately and concisely all of its essential properties of interest for a given set of concerns • We don’t see everything at once • What we do see is adjusted to human understanding Architects view Plumber’s view Electrician's view Designer ’s view Builder ’s view
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The University of Sydney Page 24 sc_outmaster<int> out1; sc_in<bool> start; // kick-start for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} SC_METHOD(generate_data); int sum; // state variable << “Sum = “ << sum << endl;} SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start void generate_data () { for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} } SC_CTOR(producer) { SC_METHOD(generate_data); sensitive << start;}}; SC_MODULE(consumer) { sc_inslave<int> in1; int sum; // state variable void accumulate (){ sum += in1; cout SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} } SC_CTOR(producer) { SC_CTOR(consumer) SC_SLAVE(accumulate, in1); sum = 0; // initialize SC_MODULE(top) // container { sc_link_mp<int> link1; A1 = new producer(“A1”); B1 = new consumer(“B1”); SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start void generate_data () { for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} } SC_CTOR(producer) { SC_METHOD(generate_data); sensitive << start;}}; SC_MODULE(consumer) int sum; // state variable SC_CTOR(consumer) { SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container { producer *A1; consumer *B1; sc_link_mp<int> link1; SC_CTOR(top) { A1 = new producer(“A1”); A1.out1(link1); B1 = new consumer(“B1”); B1.in1(link1);}}; SC_CTOR(consumer) { SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container sc_link_mp<int> link1; SC_CTOR(consumer) SC_SLAVE(accumulate, in1); sum = 0; // initialize SC_MODULE(top) // container sc_link_mp<int> link1; A1 = new producer(“A1”); A1.out1(link1); B1 = new consumer(“B1”); B1.in1(link1);}}; SC_CTOR(consumer) SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container producer *A1; consumer *B1; sc_link_mp<int> link1; SC_CTOR(top) { A1 = new producer(“A1”); A1.out1(link1); B1 = new consumer(“B1”); B1.in1(link1);}}; SC_MODULE(producer) { sc_outmaster<int> out1; sc_in<bool> start; // kick-start void generate_data () for(int i =0; i <10; i++) { out1 =i ; //to invoke slave;} SC_METHOD(generate_data); sensitive << start;}}; SC_MODULE(consumer) { sc_inslave<int> in1; int sum; // state variable void accumulate (){ sum += in1; cout << “Sum = “ << sum << SC_CTOR(consumer) SC_SLAVE(accumulate, in1); sum = 0; // initialize }; SC_MODULE(top) // container { producer *A1; What About Software? – An abstract representation of a software system Comp1 Arbiter Comp2 Comp3 Display
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The University of Sydney Page 25 Models of Software – Software model : An engineering model (specified using a modeling language) of some software that represents: 1. Design-time views of the software: The structure and content of the software specification 2. Run-time views of the software: the structure and behavior of the software in execution B A B 0..* C 0..1 0..* «import» 0..* Left Right m1 m4 m2 m3 One of the primary motives for many modeling languages is the need to more clearly represent software in execution Design-time view Run-time view
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The University of Sydney Page 26 refine NotStarted Started start producer Modern MBSE Development Style – Models can be refined continuously until the application is fully specified the model becomes the system that it was modeling! « sc_method » producer start out1 NotStarted Started start producer St1 St2 void generate_data() {for (int i=0; i<10; i++) {out1 = i;}} /generate_data( )
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The University of Sydney Page 27 A Unique Feature of Software – A software model and the software being modeled share the same medium — the computer – Which also happens to be our most advanced and most versatile automation technology Software has the unique property that it allows us to directly evolve models into implementations without fundamental discontinuities in the expertise, tools, or methods !
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The University of Sydney Page 28 The Model-Based Engineering (MBE) Approach – An approach to system and software development in which software models play an indispensable role – Based on two time-proven ideas: switch (state) { case‘1:action1; newState(‘2’); break; case‘2:action2; newState(‘3’); break; case’3:action3; newState(‘1’); break;} (2) AUTOMATION S1 S3 S2 e1/action1 e2/action2 e3/action3 switch (state) { case‘1:action1; newState (‘2’); break; case‘2:action2; newState (‘3’); break; case’3:action3; newState (‘1’); break;} (1) ABSTRACTION S1 S3 S2 e1/action1 e2/action2 e3/action3 Realm of modeling languages Realm of tools
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The University of Sydney Page 29 The Concept of “Platform Independence”? – A highly desirable objective – Separation of concerns – focus on the functionality and behaviour of a system – Enables portability 29 “Platform Independent” Software Application Computing Platform N Computing Platform 2 Computing Platform 1 . . . «deploy» «deploy» «deploy» Does “platform independence” mean that we can ignore platform concerns when designing our application?
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The University of Sydney Page 30 MBE Opportunity: Code Generation – The accidental complexity of current programming languages can be greatly reduced by the appropriate use of computer-based automation 30 S1 S3 S2 e1/action1 e2/action2 e3/action3 switch (state) { case‘1:action1; newState (‘2’); break; case‘2:action2; newState (‘3’); break; case’3:action3; newState (‘1’); break;} NotStarted Started start St1 St2 …and what about more advanced modeling languages?? Automatic code generation BUG
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The University of Sydney Page 31 MBE Opportunity: Exploiting Formal Methods – Given the possibility of making modelling language constructs better behaved than programming language constructs, it is possible to exploit formal methods for modelling language, which is impossible for programming languages because they could not handle the semantic complexity of programming languages . – E.g., State machines, Petri nets – Model checking, theorem proving – We need to work on formal semantics of modeling languages
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The University of Sydney Page 32 Learning From Models X = cos (h + p/2) + x*5 X = cos (h + p/2) + x*5 ? ▪ By formal analysis – reliable (provided the models are accurate) ? X = cos (h + p/2) + x*5 X = cos (h + p/2) + x*5 ▪ By execution – more reliable than inspection – direct experience/insight X = cos (h + p/2) + x*5 X = cos (h + p/2) + x*5 ? ▪ By inspection – mental execution – unreliable
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The University of Sydney Page 33 MBE Opportunity: Model Execution – D. Harel: “Models that are not executable are like cars without engines” – Ability to execute a model on a computer and observe its behavior – Key capabilities – Controllability: ability to start/stop/slow down/speed up/drive execution – Observability: ability to view execution and state in model (source) form – Partial model execution: ability to execute abstract and incomplete models – Executable specifications: overcoming the limitation of paper- based specifications
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The University of Sydney Page 34 MBE Opportunity: Model Transformations – Multiple purposes: – Model viewing: abstraction and refinement – Domain-to-domain model transformations: e.g., UML to queuing network model – Model-to-code transformations (code generation) – Standard: OMG’s Queries, Views, and Transformations (QVT) – We need a comprehensive theory of model transformations comparable to compiler theory
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The University of Sydney Page 35 Styles of MBSE “Who cares about the code?” Model Model only “What’s a model?” Code Code only “The code is the model” Model Code Code Visualization visualize “The model is the code” Model Code Model-centric generate Levels of Abstraction Automation Time “Manage code and model” Model Code Round Trip Engineering synchronize
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The University of Sydney Page 36 Overview – The Problem – The Premise – The Results
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The University of Sydney Page 37 Major Telecom Equipment Manufacturer – MBE technologies used – UML, Rational Technical Developer, Rational Unified Process – Example 1: Radio Base Station – 2 Million lines of C++ code (87% generated by tools) – Example 2: Network Controller – 4.5 Million lines of C++ code (80% generated by tools) Benefits a. fewer bugs b. productivity increase
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The University of Sydney Page 38 Copilot – AI pair programmer from OpenAI – https://copilot.github.com/ – Translate natural language to code – Trained by the GPT-3 model
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The University of Sydney Page 39 If this stuff is so good, why isn’t everybody doing it?
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The University of Sydney Page 40 Root Causes of Low Adoption Rate – Categories of impediments – Technical problems: modelling language lacks of mathematical foundations. – Social/Cultural issues :e.g. Many practitioners remain unaware of the potential and achievements of MBE – Economic factors: e.g. Markets focus on short-term return on investment
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