Featureous: An Integrated Approach To Location, Analysis And Modularization Of Features In Java Applications

Ph.D. Defense:

Featureous: An Integrated ApproachTo Location, Analysis And Modularization

Of Features In Java Applications

Andrzej Olszak

Road to this defense

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Publications

Main Publications

• [TOOLS’12] Olszak, A., Bouwers, E., Jørgensen, B. N. and Visser J. (2012). Detection of Seed Methods for Quantification of Feature Confinement. In TOOLS’12: Proceeding of the TOOLS Europe: Objects, Models, Components, Patterns, Springer LNCS, Vol. 7304, pp 252-268.

• [CSMR’12] Olszak, A. and Jørgensen, B. N. (2012). Modularization of Legacy Features by Relocation and Reconceptualization: How Much is Enough? In CSMR’12: Proceedings of the 16th European Conference on Software Maintenance and Reengineering, IEEE Computer Society Press, pp. 171-180.

• [SCICO’12] Olszak, A. and Jørgensen, B. N. (2012). Remodularizing Java Programs for Improved Locality of Feature Implementations in Source Code. Science of Computer Programming, Elsevier, Vol. 77, no. 3, pp. 131-151.

• [CSIS’11] Olszak, A. and Jørgensen, B.N. (2011). Featureous: An Integrated Environment for Feature-centric Analysis and Modification of Object-oriented Software. International Journal on Computer Science and Information Systems, Vol. 6, No. 1, pp. 58-75.

• [SEA’10] Olszak, A. and Jørgensen, B. N. (2010). A unified approach to feature-centric analysis of object-oriented software. In SEA’10: Proceedings of the IASTED International Conference Software Engineering and Applications, ACTA Press, pp. 494-503.

• [AC’10] Olszak, A. and Jørgensen, B. N. (2010). Featureous: Infrastructure for feature-centric analysis of object-oriented software. In AC’10: Proceedings of IADIS International Conference Applied Computing, pp. 19-26.

• [FOSD’09] Olszak, A. and Jørgensen, B. N. (2009). Remodularizing Java programs for comprehension of features. In FOSD’09: Proceedings of the First International Workshop on Feature-Oriented Software Development, ACM, pp. 19-26.

Short Papers and Tool Demo Papers

• [WCRE’11a] Olszak, A., Jensen, M. L. R. and Jørgensen, B. N. (2011). Meta-Level Runtime Feature Awareness for Java. In WCRE’11: Proceedings of the 18th Working Conference on Reverse Engineering, IEEE Computer Society Press, pp. 271-274.

• [WCRE’11b] Olszak, A. and Jørgensen, B. N. (2011). Understanding Legacy Features with Featureous. In WCRE’11: Proceedings of the 18th Working Conference on Reverse Engineering, IEEE Computer Society Press, pp. 435-436.

• [ICPC’10] Olszak, A. and Jørgensen, B. N. (2010). Featureous: A Tool for Feature-Centric Analysis of Java Software. In ICPC’10: Proceedings of IEEE 18th International Conference on Program Comprehension, IEEE Computer Society Press, pp. 44-45.

Agenda

1. Motivation

2. Overview of Featureous

3. Feature Location

4. Feature-Oriented Analysis

5. Demo

6. Feature-Oriented Remodularization

7. Automating Measurement of Features

8. Conclusions

Motivation

The Role of Modularity

•‘Proper’ modularization facilitates:• Comprehension: understanding systems one module at a time• Change: modifying modules independently•Work division: dividing work on modules boundaries

•There are various criteria for dividing software into modules







The Role of Features

Evolution &maintenance

60-90%

Initial development10-40%



60-90%


Functionality-oriented evolutionary changes

75%

Other evolutionary changes25%


• Feature – unit of user-identifiable functionality of software

• Feature-oriented change in nutshell:

– User RequestDeveloperCode


60-90%


Functionality-oriented evolutionary changes

75%

Other evolutionary changes25%

Features in OO software

• Features as inter-class collaborations:– Implicit mappings and boundaries– Scattered (increased change scope and delocalization effects)– Tangled (increased change propagation and interleaving effects)

RQ: How can features of Java applications be located, analyzed and modularized to support comprehension and modification during software evolution?

Overview of Featureous

Conceptual model of Featureous

• Layered conceptual model

– Incremental design, implementation and evaluation

The Featureous Workbench

• Tool-based approach – implemented as NetBeans plugin

• Applicative studies as evaluation

Featureous Location

The Challenge of Feature Location

• Feature location – identifying source code units that implement features

• Manual approaches

– Problems with scaling and reproducibility

• Existing semi-automated approaches

– Require dedicated test suites

– Rely on artifacts other than source code

The Method

• Dynamic analysis – execution tracing

– Resolutions of polymorphism and conditionals

• The notion of feature-entry points

– Annotated “entrances to features” to guide tracing

Feature Tracer with AspectJ LTW


@


@

Reflect


@

Reflect @

Evaluation

BlueJ JHotDraw SVG

Application size 78 KLOC 62 KLOC

Number of identified use cases 127 90

Number of identified features 41 31

Number of feature-entry points 228 91

Class code coverage 66% 75%

Total time 8 hours 5 hours

Estimated manual location time* 111-195 hours 89-155 hours

• Applied to 6 medium-sized unfamiliar OSS

– Discussed cases of BlueJ and JHotDraw SVG

Featureous Analysis

Features as Units of Code Analysis

• Feature-oriented analysis treats features as first-class code investigation entities

• Several metrics and visualizations exist– Not always compatible with one another

– Need firm evolutionary grounding

– Lack of usable implementations

Structuring Feature-Oriented Analysis

• Unifying conceptual framework for describing views

– Granularity, Perspective, Abstraction

g1

package

Abstraction

Granularity

g2

classg3

method

p1

feature

p2

code

unit

p3

feature

relation

s

a1 traceability

a2 correlation

a3 characterization

Persp

ective

• Objective definition of views

• 3x3x3 possible configurations

Instantiating the Framework

Request for change

Planning phaseChange

implementationVerificationand validation

Software comprehension

Change impact analysis

Restructuring for change

Change propagation

Granularity n/a g2

g1

g2

g1

g3

g2

g1

g3

g2

g3

g2

…


Request for change






Change propagation

Granularity n/a g2

g1

g2

g1

g3

g2

g1

g3

g2

g3

g2

…

Feature-aware source code editor

Feature-code correlation graphFeature-code correlation grid

Feature-code 3D characterization

Feature relations characterization

Feature inspector

Feature call-tree


Request for change






Change propagation

Granularity n/a g2

g1

g2

g1

g3

g2

g1

g3

g2

g3

g2

…

Feature-aware source code editor

Feature-code correlation graphFeature-code correlation grid

Feature-code 3D characterization

Feature relations characterization

Feature inspector

Feature call-tree

…

Evaluation

1. Parnas’ KWIC textbook example– 4 modularization alternatives– Results consistent with

analyses of Parnas and Garlan

2. Feature-Oriented comprehension of JHotDraw SVG

3. Adoption of feature-oriented change in JHotDraw SVG4. Analytical evaluation of support for comprehension with the

framework of Storey et al.

Shareddata

Abstractdata type

Implicit invocation

Pipesand filters

Scattering 0.29 0.35 0.20 0.20

Tangling 0.18 0.30 0.15 0.15

Change in function [23] + - + +

Demo of Featureous Workbench

Featureous Remodularization

Modularizing Features in Existing Systems

• Feature-oriented analysis alone does not reduce the scope of changes

• Physical modularization of features needed for:

– Reducing comprehension scope

– Reducing change propagation

– Facilitate per-feature division of work

Restructuring to Features

• Two fundamental class-level refactoring types

– Class relocation• Simple

• Does not untangle classes

– Class reconceptualization• Can be complex

• Untangles classes

• Fragility of class-level decomposition

– Domain entities as aggregates of feature-roles

– Problematic semantics of entity-fragments

Feature-Oriented Structure of Applications

“UI”

“Logic”

“Domain Model”

Evaluation: the Case of NDVis

• Neurological analysis tool by VisiTrend:

– Monolithic -> NetBeans MS, to improve customizability and reuse of core algorithms

– 10 KLOC, 27 use cases, 10 features

– No unit tests, unfamiliar source code, complex domain

• Application of Featureous:

– Initial analysis + Iterative modularization

Modularization in 35 Hours

• Explicit and pluggable features

• Reusable core

• Biggest challenge: decentralizing initialization of features

3rd-Party Metrics:

Scattering 16%

Tangling 27%

Cohesion 31%

Coupling 5%

Automated Remodularization

• Scalability

• Automated reverse transformation needed

– Minimizing the adoption risk

– On-demand remodularization

Remodularization as a Multi-Objective Optimization Problem

• Automatically relocating classes to optimize measurable design-level principles

– Metrics As Fitness Functions criteria

feature non-common reuse

Multi-Objective Grouping Genetic Algorithm

• Evolving a population of package structures

• Pareto-optimality

– Diverse metrics

– Avoiding extremes

• Grouping operators

• Resulting designs established (and reversed) using automated code transformations

Evaluation

JHotDraw SVG + 3 others Revisiting NDVis– Manual vs. Automated

– Role of reconceptualization

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0.005

0.01

0.015

0.02

0.025

0.03

fsca

Feature

JHotDraw SVGScattering -55%

Original

Automated

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0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

fta

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Package

JHotDraw SVGTangling -60%

Original

Automated

0

0.05

0.1

0.15

0.2

0.25

0.05 0.1 0.15 0.2 0.25 0.3

FT

AN

G

FSCA

NDVisScattering and Tangling

Original

Manual

Automated

Manual+Automated

Automated split

Automating Measurement of Features

Towards Feature-Oriented Quality Assessment

• For usage in benchmarks, feature-oriented measurement needs to be adapted to:

– Scaling to hundreds of large-scale systems

– Assuming only source code available

Automated Detection of Seed Methods

• Execution tracing static call-graph slicing

• Feature-entry points Automatically detected Anonymous seed methods

keyPressed

actio

nP

erfo

rme

d

getPresentationName

wrapViewComponent• Detection heuristic:

– Method with popular names• Exploits regularity

– Methods yielding large slices• Filters ‘shallow’ methods

Evaluation

1. Assessing extent and accuracy of coverage

• Ground truth constructed from14 diverse OSS

– Apart from expected, the method detected application-specific abstractions

Intersection79%

Method-only6%

Non-covered13%

GT-only2%

2. Measured scattering and tangling in Checkstyleover 10 years (27 releases) period

Conclusions

Open Questions and Future Directions

• Further empirical evaluation of the approach

• Continuous analysis and remodularization

• Featureous within software lifecycle

• Involving 3rd-party application authors

• Towards general on-demand remodularization

– Other concern dimensions needed

Conclusions

• Featureous delivers practical means of:

– Feature location based on feature-entry points

– Structured feature-oriented analysis

– Manual and automated remodularization

• Applicable to existing unfamiliar systems

• Real-world migration to feature-oriented modularizations feasible

– Mix automated and manual

Q&A

[Backup slides]

Featureous Workbench Design

Remodularization workbench

Feature trace model

TraceModel

featureID: String

Type

signature: String package: String instances: Set<String>

*

Execution

signature: String featureEntryPoint: boolean constructor: boolean executionCount: int

Invocation

*

*

callee

enclosingType

caller

Application: tracking evolution of feature confinement

• Checkstyle, 10 years, 27 releases: 1.0 – 5.4– Violation detectors are the features

• Scattering and tangling calibrated on 55 systems

Architectural restructuring

No erosion despite 2x

growth in LOC

Removal of J2EE detectors, refactorings –less tangling

more scattering

Details of Manual vs. Automatic

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0.01

0.02

0.03

0.04

0.05

0.06

0.07

fsca

Feature

NDVisScattering

Original

Manual

Automated

0

0.005

0.01

0.015

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0.025

0.03

0.035

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0.045

fta

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Package

NDVisTangling

Original

Manual

Automated

Results

• Average results:

• Interesting cases:

Intersection79%

Appr.-only6%

Non-covered13%

GT-only2%

k9mail Checkstyle Spring

process verify

invoke

Fragile domain entities

Technology

Featureous: An Integrated Approach To Location, Analysis And Modularization Of Features In Java Applications