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