Ph.D. Dissertation

1. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Domain-Specific Modeling and Code
Generation for Cross-platform Mobile and
IoT-based Applications
Ph.D. Candidate:
Eric Umuhoza
Advisor:
Prof. Marco Brambilla

2. Eric Umuhoza, Ph.D. Candidate
Agenda
 Introduction
 Barriers to MDD Adoption
 User Interaction Modeling Language
 Code Generation Strategies
 Model Driven Framework for User Behavior Analysis
 Questions and Answers

3. Eric Umuhoza, Ph.D. Candidate
Context
 More than 2.6 billion of smart-phone users by 2020
 Increasing number of mobile apps
 5 million apps expected in Apple App store by 2020
 App revenues expected to reach 92 billion US
dollars by 2018
Motivation

4. Eric Umuhoza, Ph.D. Candidate
Research objective
Apply MDD to all Phases of Software Development
 Design Time
 Domain-Specific Modeling Languages for: Mobile and IoT
 Generation Time
 Code Generation Strategies
 Execution Time
 Model Driven Analytics Framework
Model-driven development (MDD)
• Models as the primary artifact of the development process
• Implementation is (semi) automatically generated from the models
Benefits
• Model once and generate for any platform of choice
• It improves the development process
• Validation of requirements

5. Eric Umuhoza, Ph.D. Candidate
Application Modeling
Mobile Applications
Contributions
IoT-based Applications
Platform Independent
Extension
(Mobile IFML)
Platform
Specific
Extension
Design
Patterns
Modeling
Tool
Platform
Specific
Extension
IoT-
Extension
Design
Patterns
Domain
Model for IoT
Implementation
Declarative rules for
Code Generation
Monitoring
Model Interpretation
Execution
Logs
Application
Model
Data Analysis and
Visualization
Database

6. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Barriers in the Adoption of Model-driven
Development Approaches
 Is Application Modeling Time Consuming?
 Modeling Languages Simplification and Adaptation

7. Eric Umuhoza, Ph.D. Candidate
Models in software development
Traditional usage
• Communication with customers and users
• Support for software design
• Task specification for programming
Models as programs
• Applications are
generated (semi)
automatically from
models

8. Eric Umuhoza, Ph.D. Candidate
What is software modeling?
Where
 Design is Understanding, reasoning, and thinking about a solution
 Drawing and tool interaction is the Expression of the design in a
modeling notation
Modeling = Drawing and tool interaction
Modeling = Design + Drawing and tool interaction

9. Eric Umuhoza, Ph.D. Candidate
Goal
Research Question:
RQ1: Ratio between Modeling Effort and Design-Thinking Effort
Understand whether it is modeling or designing that
dominates the effort for creating UML model

10. Eric Umuhoza, Ph.D. Candidate
Experiments
Assumption : Participants do not think about the solution in phase β
Design Time DT = Tα – Tβ
Model Drawing Time MDT = Tα - DT
Design Time Percentage (DTP) = DT/ Tα
A two-phase experiment
 Phase α: create a domain model that addresses the assignment
Tα = Effort spent during Phase α = Design Time + Model Drawing Time
(Eq.2)
 Phase β: re-draw the same model as copy of the diagram produced in phase α
Tβ = effort spent during phase β
(Eq.1)
(Eq.3)
(Eq.4)
(Eq.5)

11. Eric Umuhoza, Ph.D. Candidate
Experiment setup
 3 application scenarios
• Every scenario describes a system to be designed
 48 users
 2 tests per user
Procedure with participants
1. Introduction
2. Instruction (test scenario)
3. Modeling assigned scenarios
1. Modeling phase
2. Redraw phase
4. User Questionnaire

12. Eric Umuhoza, Ph.D. Candidate
Results analysis
Ratio between Modeling Effort and Design-Thinking Effort
Modeling effort BUT
• Design leads to software success
• Reuse
• Maintenance
 Model vs code
• Model-driven development
 Implementation from models
DSLs  Productivity
 The fault of supposedly unproductive processes should not be blamed on modeling
 But to (anyhow necessary) time devoted to thinking about the problem and identifying the solution

13. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Barriers in the adoption of model-driven
development approaches
Modeling Languages Simplification and Adaptation

14. Eric Umuhoza, Ph.D. Candidate
 Adaptation of modeling languages
• Standard languages are complex (E.g.: BPMN)
• No perfect match of the domain to be modeled (E.g.: UML)
 Other approaches towards simplification
• New DSLs
• Extending an existing base language
 Our approach
• Simplify existing language according to the user needs
• Less time consuming and error prone than building a DSL from scratch
• Gain in Experience, Tools, and tutorials
DSLs Simplification Problem - Motivation

15. Eric Umuhoza, Ph.D. Candidate
End-userLanguagedesigner
User
Questionnaire
Language
Evaluation
Definition of
Language
Variants
Modeling of
Use Cases
Selection of
Reducible
Language Elements
General
Language
Simplified
Language
DSLs Simplification Problem - Process

16. Eric Umuhoza, Ph.D. Candidate
DSLs Simplification Problem – Case study
 Simplification of BPMN for Personal Process Modeling
 Goal Study “how much is enough” for
• End users
• Collaborative planning and execution
Michael zur Muehlen and Jan Recker "How much language is enough? "
 Less than 20% of
BPMN constructs
are used regularly

17. Eric Umuhoza, Ph.D. Candidate
End-userLanguagedesigner
User
Questionnaire
Language
Evaluation
Definition of
Language
Variants
Modeling of
Use Cases
Selection of BPMN
Elements
BPMN Simplified
BPMN
DSLs Simplification Problem - Process

18. Eric Umuhoza, Ph.D. Candidate
Selection of BPMN Elements
 Selected 8 elements from 52 constructs of BPMN

19. Eric Umuhoza, Ph.D. Candidate
Language Variants Definition
 Defined 4 language variants

20. Eric Umuhoza, Ph.D. Candidate
 Experiment setup
• 3 application scenarios
• 4 syntax variants
• 24 users
• Multiple tests per user
DSLs Simplification Problem - Validation

21. Eric Umuhoza, Ph.D. Candidate
Results Analysis
• Average modeling time
Language Variants
Duration(s)
~19 min
~16 min
~21 min
Language Variants
#Elements
• Average # of used concepts

22. Eric Umuhoza, Ph.D. Candidate
Results Analysis
Language Variants
#opinions
Easy Med Hard
• Explicit feedback on language
variants complexity
Variant 1
Simpler, faster, less errors, limited power
(no conditions)
Variant 2
Strong thanks to looping, a lot of errors
Variant 3
Good compromise.
Limited by single local parameter
Variant 4
Harder, slower, more errors. Multiple local
parameters not appreciated
• Rule of “thumb” on Language Variants

23. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
User Interaction Modeling Language
Mobile Modeling Language

24. Eric Umuhoza, Ph.D. Candidate
Mobile Modeling Language
 A platform independent modelling (PIM) language
• A mobile-specific extension of IFML
• Covering mobile-specific requirements
• Amenable to code generation
 Why IFML?
• a OMG standard for User Interaction Modeling
• Technology-independent
• It is extensible

25. Eric Umuhoza, Ph.D. Candidate
Interaction Flow Modeling Language (IFML)
Album
Search
«Window» AlbumSearch
Album
List
«Window» Albums
Album
Details
«Window» Album
«ParameterBindingGroup»
Title  AlbumTitle
Year  AlbumYear
«ParameterBindingGroup»
SelectedAlbum  AnAlbum
«Form» «List» «Details»
EventViewContainer
View Component
Album
Deletion
«ParameterBindingGroup»
SelectedAlbum  AnAlbum
Action
Navigation Flow
ParameterBinding

26. Eric Umuhoza, Ph.D. Candidate
Mobile–specific extension of IFML
 Four main sets of mobile-specific
concepts
1. Mobile Containers and Components
2. Events
• User interactions
• Device’s features
• Sensors
3. Mobile Actions
4. Mobile Context
Bottom-up approach
Investigation of
Mobile
Platforms
Extraction of
Common
Features
Abstraction
and
Conceptualization
 Meta-model excerpts:

27. Eric Umuhoza, Ph.D. Candidate
Mobile IFML - Events
 Events generated by the
interaction of the user
(gestures, …)
 Mobile container
MobileContainer
MobileEvent
«Screen» List
«List» Lists
Options
«LongPress»
«Screen» Options
«Details» List
Edit list Delete list

28. Eric Umuhoza, Ph.D. Candidate
Mobile IFML – Access to native features
MobileComponent
MobileAction MobileActionEvent
 Access to system features
 Native functions
 Phone sensors

29. Eric Umuhoza, Ph.D. Candidate
Mobile IFML – Mobile Context
 It assumes particular relevance in mobile apps
 The context must gather all the dimensions that characterize:
• The user's intent
• The capacity of access device
• The communication network, and etc.

30. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
User Interaction Modeling Language
IOT Integration

31. Eric Umuhoza, Ph.D. Candidate
IoT Integration- Overview
 Interactions between the user and the IoT systems in two phases
 User – Terminal
 Terminal – IoT Devices

32. Eric Umuhoza, Ph.D. Candidate
IoT Extensions - Components
Get Action Set Action Plan Action
Action SystemEvent
Single Information
Event
Approaching Event
 IoT Actions
 IoT Events (approaching, repeat, etc.)
• No new ViewComponent and ViewContainer
 User interaction patterns …
 Meta-model excerpts:

33. Eric Umuhoza, Ph.D. Candidate
IoT user interaction patterns – Set
Make
Cappuccino
Turn on
100°C
 Set patterns
• One device – One operation
• One device – More operations
• More devices – One operation
• More devices – More operations
• One device – One Program

34. Eric Umuhoza, Ph.D. Candidate
IoT user interaction patterns – Get
• State of a device
• Details of a device
• Information from device
• Search device
• Nearby devices
• …
 Get patterns

35. Eric Umuhoza, Ph.D. Candidate
Patterns- based UI modeling
 Get Information from the Device
 Get State of the Device
 Get Details of a Device
 Get Information from whole Category
 One Device One Operation
 Store Information
 Push Information

36. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Implementation:
Code Generation Strategies

37. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
 CIM to several PIMs
 PIM to several PSMs
 Many other combinations
 Skipping levels
Model-driven architecture (MDA)
Application
Code
Model-to-Text
Transformation
(M2T)

38. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
PIM
Native
Code
M2TM2M
PSM
PIM
Native
Code
M2T
Native
Code
M2T
PSM
PIM
Cross-platform
Code
M2T
M2T
FSM
Cross-platform
CodePIM
M2M
(1)
(2)
(3)
(4)
(5)
 Data-driven native
applications

39. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
PIM
Native
Code
M2TM2M
PSM
PIM
Native
Code
M2T
Native
Code
M2T
PSM
PIM
Cross-platform
Code
M2T
M2T
FSM
Cross-platform
CodePIM
M2M
(1)
(2)
(3)
(4)
(5)
 Native apps with
complex device-
specific functionalities

40. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
PIM
Native
Code
M2TM2M
PSM
PIM
Native
Code
M2T
Native
Code
M2T
PSM
PIM
Cross-platform
Code
M2T
M2T
FSM
Cross-platform
CodePIM
M2M
(1)
(2)
(3)
(4)
(5)
 Native apps for one
platform

41. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
PIM
Native
Code
M2TM2M
PSM
PIM
Native
Code
M2T
Native
Code
M2T
PSM
PIM
Cross-platform
Code
M2T
M2T
FSM
Cross-platform
CodePIM
M2M
(1)
(2)
(3)
(4)
(5)
 Multiple platform
availability (and time
to market) is more
important than high
performance
 Loose in nativity
• Gain market
share

42. Eric Umuhoza, Ph.D. Candidate
Code generation strategies
PIM
Native
Code
M2TM2M
PSM
PIM
Native
Code
M2T
Native
Code
M2T
PSM
PIM
Cross-platform
Code
M2T
M2T
FSM
Cross-platform
CodePIM
M2M
(1)
(2)
(3)
(4)
(5)
 FSM :Cross-platform
Framework Specific Model
 Team with low
experience in Targeted
tool

43. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Application Monitoring:
Model-Driven Framework for User
Behavior Analysis

44. Eric Umuhoza, Ph.D. Candidate
Model-driven user behavior analysis
 Web analytics tools provide reports regarding web site usage
• Page visits, Visitors, Navigation
• Statistics about the content of the pages?
• Event tracking of GA tracking some content level interactions
• But
• Tracking code placed at each position the event could appear
• Limited set of events
 Our approach
• Component level analysis (also without navigations)
• Enriched analytics by querying the instances of DB
• All components, but the ones hidden on purpose, are
displayed
• Visualization on models

45. Eric Umuhoza, Ph.D. Candidate
User Behavior Analytics Approach
Example of WebRatio Runtime log line.RTXLog,
22 Jun 2016 11:10:51,761 DEBUG [http-bio-8080-exec-5] com.webratio.rtx.core.ServiceProvider:45) -
[119354A67C7C0177D4A7F411E75BCDE7][page21][pwu6Block] Creating service: WEB-INF/descr/pwu6Block.descr
Example of enriched Log line
[119354A67C7C0177D4A7F411E75BCDE7][ViewContainer[Type:page,id:page21,..]][List[id=pwu6Block][dataBinding
=Book]][[title:"...",author:"...",...]]

46. Eric Umuhoza, Ph.D. Candidate
 Possible implementations
 Elasticsearch, Logstash, and Kibana
 Microsoft Azure (HDInsights, Apache Spark)
 Etc.
Approach - Analysis and Visualization
 Three kinds of user behavior analytics
 Navigation-based analytics:
• Number of visits. entrance rate, Incoming Link Ratio, Outgoing Link Ratio
 Content-based analytics
• Top k visualized instances
• Top clicked instances
 Structure-based analytics
 Visualization
 Traditional data visualization tools
• Pie, bar, navigation flow charts
 Colored model

47. Results Visualization
 Enhancing User Experience
 Refactoring
 Repositioning
 Etc.

48. Eric Umuhoza, Ph.D. Candidate
Conclusions and Future Directions
 Model-driven development of mobile and IoT applications
 Design time
• Domain-specific modeling languages for Mobile and IoT
• Design methodology – user interaction patterns
 Implementation
• Code generation strategies
 Execution time
• Model driven user behavior analysis
 Solutions to barriers to model-driven development adoption
 Future Directions
 Implementation of IoT – based Apps
 Language Simplification and adaptation
 Dissecting Design Effort and Modeling Effort in MDD Approaches
 User Behavior Analytics

49. Firma convenzione
Politecnico di Milano e Veneranda Fabbrica
del Duomo di Milano
Aula Magna – Rettorato
Mercoledì 27 maggio 2015
Domain-Specific Modeling and Code Generation
for Cross-platform Mobile and
IoT-based Applications
Thanks