1. Towards a framework for architectural design decision support
Manoj Mahabaleshwar, Garching,

2. Overview A framework for architectural knowledge management
(domain models, rule engine, and visualization components)
- Automatic annotation of architectural-significant elements
- Population & recommendation of architecture alternatives and software solutions to realize architectural design decisions
- Extract architectural decisions (focus on technical decisions) from issue management system
- Classify them as either structural, behavioral, or banned decisions
- Extract – who raised the concern, who took the decision and when was the decision made from the issue management system (what is implicit)
- Build user profiles based on the above information
Use the AKM model, user model, and a set of simple heuristics to predict the possible architectural decisions that will be taken by an architect
Manoj (© Florian Matthes, 2016)

3. A framework for architectural knowledge management
SyncPipes
REST API
Access control
Rule engine (Reasoning knowledge)
Model-based expressions
Events & notifications
Meta-model
Static knowledge model
Dynamic knowledge model
Static knowledge base
Dynamic knowledge base
Rules manager
Value
Added
Services
IR-application
Platform
Client Apps
Source tools
Model designer
Non-structural data
Bhat, M.; Shumaiev, K.; Biesdorf, A.; Hohenstein, U.; Hassel, M.; Matthes, F.: Meta-model Based Framework for Architectural Knowledge Management, SAGRA workshop at European Conference on Software Architecutre 2016, Nov Dec. 02, 2016, Copenhagen, Denmark
Manoj (© Florian Matthes, 2016)

4. Architecture design decision model
Manoj (© Florian Matthes, 2016)

5. SyncPipes A platform for implementing reusable adapters
Tool A
Data model A
SC KB
SC model
SyncPipes Configurator
SyncPipes Mapper
SyncPipes Execution
Input configuration
Output configuration
Sync
configuration
SyncPipes Synchronizer
establish connection
concept mapper
attribute mapper
Get Types
and Properties
Get Entities
Create Entities
Update Entities 1 2 3 4
Manoj (© Florian Matthes, 2016)

6. An ontology-based approach for software architecture recommendations
Manoj (© Florian Matthes, 2016)

7. Automatic extraction and classification of design decisions from issue management systems
Issues
Decision detector
Decision
Decision classifier
Structural decision
Decision detection model
Decision classification model
load
uses
classify
Behavioral decision
Ban decision
Not a decision
Manoj (© Florian Matthes, 2016)

8. Next steps (1)
Automatic extraction of relationships between design decisions
Focus on extracting relationship between design decisions (influences, refinedBy, decomposesInto, compatibleWith, incompatibleWith)
Important for
Defining dependencies between ADDs
Specifying constraints on ADDs
Visual representation of constraint graph model
Specify constraints using an expression language
Modeling constraints required for predication purposes
ADTopic
ADIssue
ADAlternative
ADOutcome
influences
refinedBy
decomposesInto
forces
compatableWith
inCompatableWith
triggers
contains
belongsTo
priority
status
preference
risk; cost
Manoj (© Florian Matthes, 2016)

9. Next steps (2) Who did what and when?
Existing work on ADD models do not consider user preferences, project context, and heuristics to support the decision making process
AIM
Extract who took the decision, how long did it take to implement, complexity of tasks involved based on source code changes
Extract a decision makers’ preferences related to technologies and types of issues handled to build user profiles.
RESULT
Combining user profiles with the ADD model should support the recommendation of
Who should be responsible to address a specific concern?
What is the cost of addressing a specific concern? ?
Use issue management system as the main input source
Focus on a specific domain (e.g. analytics domain) and system (e.g. component-based)
Manoj (© Florian Matthes, 2016)

10. ML model for decision detection and classification
Big picture
Web-based editor
Software architects
Software developers
current project
SyncPipes
ADD explorer
ADD recommender
load project
recommend alternative
architectural solution
relationships between ADDs
AMELIE
ADDs to address similar concerns
users who have addressed similar concerns
estimated effort to address similar concerns
read architecture
description
read labeled issues
annotate text with
meta-information
apply ML model
for new issues
load projects
SyncPipes
Document Classifier
Document Annotator
Facilitators
Domain experts
past projects
generate
uses
ML model for decision detection and classification
Manoj (© Florian Matthes, 2016)

11. Manoj Mahabaleshwar MSc. Research Assistant 18162
Manoj (© Florian Matthes, 2016)