Tool Mec.

Tool Elec.

Workflow

Analysis

SCADA

Tool SW

Engineering Environment Integration Engineering Environment Integration Across DisciplinesAcross Disciplines

with the Engineering Service Buswith the Engineering Service BusDietmar Winkler Stefan Biffl

Christian Doppler Laboratory SE-Flex-ASInstitute of Software Technology and Interactive Systems (ISIS)

Vienna University of Technologyhttp://cdl.ifs.tuwien.ac.at

Model Mec.

Model SW

Model Elec.

2

End-to-End Test Across Engineering Models

Use of common concepts in models across engineering disciplines

Sensor

V_AS2S3S4S5

V_BV_CV_D

S1

SystemInterface

SoftwareInterface

Software„Behavior“

Wiring Configuration Use of Data

Electrical Engineer Configurator Software Engineer

End-to-End AnalysisList of sensor name/description/type with Variable name/description/typeWarnings for incomplete chains between variables and sensors

System interface

Sensor

- Name- Description- Type

Software Interface

- Var_Name- Var_Descriptn- Var_Type

Wire Configuration Link

Electrical Engineer

Configurator

Software Engineer

3

Motivation and Overview

Software-intensive systems.

Several disciplines cooperate in an industry environment.

Engineering models (e.g., mechanics and electronics) contain requirements and design constraints for software engineers.

Existing models and tools focus on supporting engineers in specific disciplines.

Human experts bridge technical and semantic gaps between models and tools of different engineering disciplines.

4

Scope of Research

What hinders effective collaboration across disciplines?

– Domain- and vendor-specific solutions (e.g., point-to-point integration).

– Heterogeneous models in various disciplines.

– Different stakeholders and different “languages”.

– Limited connection between development and operation.

Concept evaluation based on real-world use cases and prototypes

– Technical Integration of Tools.

– Semantic Integration of Data.

– Quality Assurance across Engineering Disciplines.

Christian Doppler Laboratory started on January 2010.

Tool Mec.

Tool Elec.

Workflow

Analysis

SCADA

Tool SW

Model Mec.

Model SW

Model Elec.

5

Software Engineering Integration for Flexible Automation Systems

Basic research challengesEarly defect detection across engineering discipline and tool boundaries.Engineering process analysis using design- and run-time data sources.

Research applications in the industry partners’ domainsPlatform to build integrated tools for automation systems development & QA.SCADA systems with data analysis for monitoring automation systems.

6

Challenges and Requirements

Challenges from weak integration of software tools for engineering1. Engineering process on event level is hard to track and analyze.2. Integration of software tools is often vendor-specific and/or fragile.3. Sharing of data models across software tools is inefficient and risky.4. Run-time defect detection cannot easily access design knowledge.5. Integration of run-time environments is hard to observe for analysis.

Requirements Management

Pipe & Instrumentation

Electrical Plan

Process Engineer

Software Engineer

Project Manager Tool Data

Tool Data

Tool DataElec. Engineer

Flexible Automation System

Software Dev. EnvironmentTool Data

Operator

Maintenance Engineer

SCADATool Data

Anfoderungs-ManagementOnsite Eng. EnvironmentTool Data

Data Analysis/Simulation

Tool Data

4

2

1

Design Time Run Time

5

3

Software Engineering Integration

7

Automation Service Bus

Technical Integration: Engineering Service Bus (1), Control Service Bus (2).Semantic Integration: Engineering Knowledge Base (3).Flexible integration of SCADA (4) with data analysis/simulation (5).Defect detection approaches for design time (6) and run time (7).

Goal: Approaches for the integration of software tools in automation engineering.

Aut

omat

ion

Serv

ice

Bus

(Offs

ite)

Engi

neer

ing

Serv

ice

Bus

ASB

(Ons

ite)

Con

trol

Ser

vice

Bus

8

Example: Technical Systems Integration and Interoperability

Approach for integrating available automation engineering tools3 heterogeneous engineering tools.Defined Workflow. E

lectrical Plan

Tool Data

Wor

kflo

w

ED

B

Issu

e D

omai

nR

egis

try

Issu

e Tr

acke

r To

olTo

ol D

ata

Issu

e Tr

acke

r To

olTo

ol D

ata

Issu

e Tr

acke

r To

ol X

Tool

Dat

a

Not

ifict

nD

omai

nR

egis

try

Not

ifica

tion

Tool

ATo

ol D

ata

Not

ifica

tion

Tool

ATo

ol D

ata

Not

ifica

tion

Tool

ATo

ol D

ata

Project

Definition

Softw

are Dev.

Environm

entTool D

ata

Project

DefinitionP

ipe &

Instrumentation

Tool Data

9

Collaboration Across Disciplines: Semantics?

9

10

Semantic Integration of Engineering Knowledge

Identification of common concepts across engineering disciplines.

„Pump flow“ Real (l/min)0 to 1,200%I20.5.3

InformationAnalog 0 to 10 VX.22.2.1

Software Engineer

Process Engineer

ElectricalEngineer

Tool A Data Model Tool B Data Model

Domain/project data model

Mechanical equipment properties

Transmission linesTerminal points

Data TypesLogical Behavior

RequirementsLocation IDsComponents

Interfaces

Tool C Data ModelSignals (I/O)

Machine vendor catalogue

Model Mapping Tool A – Domain

Derived MappingTool A – Tool B

Models

Common concept

11

Defect Detection Across Tool Boundaries and Disciplines

Use of common concepts in models across engineering disciplines

12

End-to-End Quality Assurance

Challenge: Defect Detection across engineering disciplines

Identification of various defect types:– Missing, wrong, inconsistent model

elements or relationships.– Conflicts from changes to overlapping

model elements.– Run-time violation of model

constraints.

Quality Assurance approaches– Review of overlapping model parts,

e.g., with inspections.– Automated check of model assertions

(syntactic and semantic).– Change conflict detection and

resolution.

Software Engineer

Process Engineer

ElectricalEngineer

RequirementsLocation IDsComponents

Interfaces

Signals (I/O)

Machine vendor catalogue

Sensor

V_AS2S3S4S5

V_BV_CV_D

S1

SystemInterface

SoftwareInterface

Software„Behavior“

Wiring Configuration Use of Data

Electrical Engineer Configurator Software Engineer

13

Engineering Process Analysis (CI&T)

Process automation, analysis and assessment based on (EngSB) event logs– Visualization of the expected engineering process.– Comparison of expected with traces of actual engineering processes.– Analysis of actual engineering process variants (frequency of paths taken).– Measurement of engineering process duration, waiting and execution times.

Example: Continuous Integration and Test (CI&T).

Start Star

t

Com

plet

eBuildCheckoutcomplete St

art

Com

plet

eTest

Star

t

Com

plet

eDeploy

Build Complete /

Failed

Test Complete /

Failed

Deploy Complete /

Failed

EndExpected CI&T process

Process analysis based on sample engineering logs..

Sta

rt

Com

plet

e

Sta

rt

Com

plet

e

Sta

rt

Com

plet

e

14

Summary

Multi-disciplinary engineering projects are prone to risks from defects and delays due to technical gaps between tools and semantic gaps between data models.Technical and semantic integration provide the foundation for engineering process automation and quality management to lower these project risks.

The Engineering Service Bus (EngSB) environment provides:– Technical Integration: Workflow-Rules and Events.– Semantic Integration: Data Models across disciplines.– Defect Detection & Engineering Process Automation:

Engineering rules and process analysis.

Industry Use Cases– End-to-End Quality Assurance.– Difference analysis between signal versions.– Defect detection in data models across tools and

engineering disciplines.– Engineering Process Automation, Analysis and

Improvement.

Model Mec.

Model SW

Model Elec.

15

Thank you ...

Engineering Environment Integration Across Disciplineswith the Engineering Service Bus

Dietmar WinklerStefan Biffl

Christian Doppler Laboratory "Software Engineering Integration for Flexible Automation Systems"

http://cdl.ifs.tuwien.ac.at

Institute of Software Technology and Interactive Systems (ISIS)Vienna University of Technology

{Dietmar.Winkler, Stefan.Biffl}@tuwien.ac.at