domain-specific modeling and code generation for cross-platform mobile and iot-based applications

Firma convenzione Politecnico di Milano e Veneranda Fabbrica

del Duomo di MilanoAula 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

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


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


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


Application Modeling

Mobile Applications

Contributions


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

ExecutionLogs

Application Model

Data Analysis and Visualization

Database




Barriers in the Adoption of Model-driven Development Approaches

Is Application Modeling Time Consuming? Modeling Languages Simplification and Adaptation


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


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


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


Experiments

Assumption : Participants do not think about the solution in phase β

Design Time DT = Tα – TβModel 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)


Experiment setup

3 application scenarios• Every scenario describes a system to be designed

48 users 2 tests per user

Procedure with participants1. Introduction2. Instruction (test scenario)3. Modeling assigned scenarios

1. Modeling phase2. Redraw phase

4. User Questionnaire


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




Barriers in the adoption of model-driven development approaches

Modeling Languages Simplification and Adaptation


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


End-

user

Lang

uage

desi

gner

UserQuestionnaire

Language Evaluation

Definition of Language Variants

Modeling of Use Cases

Selection of Reducible

Language Elements

General Language

Simplified Language

DSLs Simplification Problem - Process


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


End-

user

Lang

uage

desi

gner

UserQuestionnaire

Language Evaluation

Definition of Language Variants

Modeling of Use Cases

Selection of BPMN Elements

BPMN Simplified BPMN

DSLs Simplification Problem - Process


Selection of BPMN Elements

Selected 8 elements from 52 constructs of BPMN


Language Variants Definition

Defined 4 language variants


Experiment setup• 3 application scenarios• 4 syntax variants• 24 users• Multiple tests per user

DSLs Simplification Problem - Validation


Results Analysis

• Average modeling time

Language Variants

Dur

atio

n (s

)

~19 min

~16 min

~21 min

Language Variants

# El

emen

ts

• Average # of used concepts


Results Analysis

Language Variants

# op

inio

ns

Easy Med Hard

• Explicit feedback on language variants complexity

Variant 1Simpler, faster, less errors, limited power(no conditions)

Variant 2Strong thanks to looping, a lot of errors

Variant 3Good compromise.Limited by single local parameter

Variant 4Harder, slower, more errors. Multiple local parameters not appreciated

• Rule of “thumb” on Language Variants




User Interaction Modeling Language

Mobile Modeling Language


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


Interaction Flow Modeling Language (IFML)

AlbumSearch

«Window» AlbumSearch

AlbumList

«Window» Albums

AlbumDetails

«Window» Album

«ParameterBindingGroup» Title AlbumTitleYear AlbumYear

«ParameterBindingGroup»SelectedAlbum AnAlbum

«Form» «List» «Details»

EventViewContainer

View Component

AlbumDeletion

«ParameterBindingGroup»SelectedAlbum AnAlbum

Action

Navigation Flow

ParameterBinding


Mobile–specific extension of IFML

Four main sets of mobile-specific concepts1. Mobile Containers and Components2. Events

• User interactions• Device’s features• Sensors

3. Mobile Actions4. Mobile Context

Bottom-up approach

Investigation of Mobile

Platforms

Extraction of Common Features

Abstraction and

Conceptualization

Meta-model excerpts:


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


Mobile IFML – Access to native features

MobileComponentMobileAction MobileActionEvent

Access to system features Native functions Phone sensors


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.




User Interaction Modeling Language

IOT Integration


IoT Integration- Overview

Interactions between the user and the IoT systems in two phases User – Terminal Terminal – IoT Devices


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:


IoT user interaction patterns – Set

MakeCappuccino

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


IoT user interaction patterns – Get

• State of a device• Details of a device• Information from device• Search device• Nearby devices• …

Get patterns


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




Implementation: Code Generation Strategies


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)



PIM Native Code

M2TM2MPSM

PIM Native Code

M2T

Native Code

M2TPSM

PIM Cross-platform Code

M2T

M2TFSM

Cross-platform CodePIM

M2M

(1)

(2)

(3)

(4)

(5)

Data-driven native applications



PIM Native Code

M2TM2MPSM

PIM Native Code

M2T

Native Code

M2TPSM


M2T

M2TFSM


M2M

(1)

(2)

(3)

(4)

(5)

Native apps with complex device-specific functionalities



PIM Native Code

M2TM2MPSM

PIM Native Code

M2T

Native Code

M2TPSM


M2T

M2TFSM


M2M

(1)

(2)

(3)

(4)

(5)

Native apps for one platform



PIM Native Code

M2TM2MPSM

PIM Native Code

M2T

Native Code

M2TPSM


M2T

M2TFSM


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



PIM Native Code

M2TM2MPSM

PIM Native Code

M2T

Native Code

M2TPSM


M2T

M2TFSM


M2M

(1)

(2)

(3)

(4)

(5) FSM :Cross-platform

Framework Specific Model

Team with low experience in Targeted tool




Application Monitoring:Model-Driven Framework for User

Behavior Analysis


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


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:"...",...]]


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

Results Visualization

Enhancing User Experience Refactoring Repositioning Etc.


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




Domain-Specific Modeling and Code Generation for Cross-platform Mobile and


Thanks

domain-specific modeling and code generation for cross-platform mobile and iot-based applications

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