Graph-based Architecture Knowledge Discovery

1. Graph-based Implicit Knowledge Discovery
Welcome
from Architecture Change Logs
Aakash Ahmad, Pooyan Jamshidi, Muteer Arshad and Claus Pahl
Presentation Title
ahmad.aakash@computing.dcu.ie
Software and System Engineering group
http://www.computing.dcu.ie/~cpahl/sse-group.htm
School of Computing, Dublin City University, Ireland
THE IRISH SOFTWARE ENGINEERING RESEARCH CENTRE

2. Agenda
- Architecture Change Mining and Change Execution
- Log-based Analysis of Architecture Change Instances
- Change Instance Formalisation
- Discovering Operationalisation, Dependencies and Patterns
- Change Pattern Graph
- Conclusions & Outlook
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3. Architecture Change Mining & Change Execution
How to exploit the architecture evolution history that allows us to foster potentially
reusable operationalisation and patterns to guide architecture evolution?
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4. Log-based Analysis of Architecture Change
Architecture Change Logs (ACL) as a knowledge base provide us with a transparent
and centrally manageable repository by maintaining fine-granular instances of sequential
change aggregating over time.
- Adequacy of Change Log Data
Completeness, Granularity of Change, etc.
- Log Data Classification
Change Data & Auxiliary Data
- Empirical Analysis of Change Instances
EBPP and TRS case studies
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5. Graph-based Formalisation of Change Instances
- Formal analysis and effiecient processing of significantly large data size
- Graph-mining as a formal approach to discover operationalisation and patterns
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6. Operationalisation and Dependencies in
Change Logs
An effective and potentially reusable solution to recurring architecture evolution problems is a
consequence of empirical discovery and not a mere invention.
A pattern can be i) identified as recurrent, ii) specified once and iii) instantiated multiple times to
support potential reuse in architecture evolution
1. Operationalisation
- Atomic
- Composite
- Sequential
2. Dependencies
- Hierarchical
- Sequential
- Order & Inordered
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7. Towards a System-of-Evolution-Patterns?
Change pattern provides a generic, first class abstraction (that can be operationalised
and parameterised) to support potential reuse in architectural change execution.
𝐼𝑁𝑉(𝑂𝑃𝑅𝑛(𝑎𝑒𝑚∈𝐴𝐸))
PAT<name, intent>: PRE(aem ∈ AE) POST(ae′m ∈ AE).
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8. Pattern-based Architecture Evolution
Step 1: Architecture Change Specification.
Step 2: Change Pattern Retrieval
Step 3: Change Pattern Instantiation
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9. Thank you for your attention.
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10. Backup
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11. Graph-based Change Pattern Notation
Pattern Specification – as an attributed typed Graph (.GML) notation.
Pattern Storage – Graph databse using Neo4j graph tuples.
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12. Step 2: Change Pattern Retrieval
(Root) <relatesTo> [Nodes] :
(ChangePattern) <isComposedOf, ConstrainedBy, Evolves> [Operators | Constraints | ArchitectureModel]
Query - Which change pattern(s) allow integration of a mediator component among two directly connected
components?
01: START pattern = node(ChangePattern)
02: MATCH (pattern) – [:ConstrainedBy] - > (Constraints)
03: – [:Composedof] - > (Operators)
04: WHERE Operators IS NOT Null
05: RETURN ChangPattern.name, ChangePattern.intent, Operators.operatorType
Listing: Cypher Query to Retrieve Pattern Name, Intent and Operationalisation
START - command to set the primary node(s),
MATCH - based on user-specified change constraints respectively.
WHERE - allows for additional conditional checking, while
RETURN - provides the gathered results.
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13. Experimental Analysis & Evaluation
1 Scenario-based Evaluation
- EBPP & TRS Evolution Cases
- Pattern Types & Adequacy
2 Prototype-based Validation
- Automated Pattern-based Evolution
- Survey & Usability Analysis
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