ARCHITECTURAL
RECOVERY TO AID
DETECTION OF
ARCHITECTURAL
DEGRADATION
Joshua Garcia*, Daniel Popescu*, Chris Mattmann* † ,
Nenad Medvidovic*, and Yuanfang Cai ‡
*University of Southern California, † Jet Propulsion Laboratory, ‡ Drexel University

Architectural Degradation

Software maintenance tends to dominate

Maintaining architectures

Architectural Degradation

Architectural drift and erosion
 Architectural smells

Architectural Recovery

4 iRODS – Prescriptive Architecture
Source: https://www.irods.org/index.php/Introduction_to_iRODS

5 iRODS – Descriptive Architecture
Legend:
Layer
Component
Two-way dependency
One-way dependency

iRODS – Prescriptive and Descriptive
Architecture

Architectural Smells in iRODS
7
Fully connected graph
Dependency
Cycle
Skipping
Layers
Skipping
Layers
Dependency
Inversion
Excessive
Load on
Component
Legend:
Layer
Component
Two-way dependency
One-way dependency
Bloated Components

Outline

Motivation

Existing Architectural Recovery Techniques

Our Approach To Architectural Recovery

Current Status

9 iRODS – Descriptive Architecture
Legend:
Layer
Component
Two-way dependency
One-way dependency

Component Recovery
10

Existing techniques
 Automated
 Heavy focus on structural dependencies
 WCA, LIMBO, ACDC, Bunch, etc.
 Can be inaccurate
 Do not represent concerns
Network
Communication
Core Library
Low-level Fabric

11 iRODS – Descriptive Architecture
Legend:
Layer
Component
Two-way dependency
One-way dependency

Recovery Techniques for Connectors


Connector types
 Interaction


Applicationindependent
Non-functional properties
Patterns and queries for connectors


ManSART, cliches, XMLbased, X-Ray, etc.
Time-consuming, manual, and error-prone iRODS Fuse
Event-based
Stream
Server Rule Engine

Outline

Motivation

Existing Architectural Recovery Techniques

Our Approach To Architectural Recovery

Current Status

Approach and its Goals

Novel technique for recovering components and connectors
 Goals:
 Automation
 Accuracy
 Understandability

Overall Approach
Source Code
Concern
Extraction
Structural
Fact
Extraction
Concerns
Structural
Facts
Application
Specificity
Classification for Concerns
Brick
Recovery
Concerns with
Application
Specificity
Component and Connector
Classification
Component/
Connector
Model
Bricks

Representing Concerns
16

Topic Models
 A statistical language model
 The source code has a set of words called the vocabulary
 Topic – probability distribution over words
 Our representation of concerns
 Document-topic distribution – probability distribution over topics
 The extent to which a document (component or connector) has a concern

17
Using Learning for Component/
Connector Classification

Selection of features
 Application-specific or application-independent concerns
 Structural features
 Design patterns

Outline

Motivation

Existing Architectural Recovery Techniques

Our Approach To Architectural Recovery

Current Status

Current Status
•
Prototyped and preliminary experiments
•
Variety of Systems
•
Apache OODT, Hadoop, Bash, Linux, etc.
•
Java and C systems

Thank You

Questions?