Challenges for addressing quality factors in model transformation
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Challenges for Addressing Quality Factors in Model Transformation. Eugene Syriani Jeff Gray. Software Engineering Group Department of Computer Science College of Engineering. University of Alabama. Motivation Challenges & Planned Solutions

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Challenges for addressing quality factors in model transformation

Challenges for Addressing Quality Factors inModel Transformation

Eugene SyrianiJeff Gray

Software Engineering Group

Department of Computer Science

College of Engineering

University of Alabama


Challenges for addressing quality factors in model transformation

  • Motivation

  • Challenges & Planned Solutions

    • Elaboration of framework for good practices & their assessment

    • Formal analysis of this framework

    • Application in industrial settings

  • Conclusion


Model transformation statu quo

Model transformation statu quo

GReAT

QVT

MOLA

DSLTrans

ProGReS


What is missing

What is missing?

  • Good practices to design transformations

    • Pragmatics, “intuitions”

    • Design patterns / anti-patterns

  • Assessments of high quality transformations

    • Quality Criteria

    • Evaluation techniques

    • Metrics


What we propose

What we propose

  • Define quality criteria based on existing transformations

  • Identify & classify well-founded MT design patterns with proven quality

  • Support MT engineers by integrating patterns in their designin an automated manner


Design pattern cataloging

Design pattern cataloging

Goal:

Build a repository of design patterns for MT development


Pattern identification

Pattern identification

  • Identify & discover recurrent patterns in model transformation

    • Completeness issue (see GoF)

    • Systematic process


Pattern identification1

Pattern identification

  • Examine a large set of data

    • Academic, Industrial

    • Repositories: ATL transformation zoo, ReMoDD, tool contests, benchmarks

    • Case studies from literature


Pattern identification2

Pattern identification

  • Discover new patterns

    • Map GoF patterns to MT paradigm

      • What does Visitor, Proxy, Composite, etc. mean?

    • Be creative!


Pattern identification caveat

Pattern identification Caveat

  • General-purpose vs. Domain-specific MT patterns

    • Copy elements from source to target model

    • Animate a state-transition modeling language

  • Language independence

    • Declarative/imperative, Unidirectional/bi-directional,Implicit/explicit control flow, In-place/out-place/exogenous/endogenous

    • Application scenarios

  • Level of granularity

    • Rule level

    • Multiple rules may be required to perform single task

    • Re-usable libraries of transformation snippets

    • Composition of patterns


Pattern formalism

Pattern formalism

  • Facilitate understanding, documenting, communicating, and reasoning about the patterns in a standard way

  • Must be language independent

    • MOF-like languages

    • Use of generics/templates

    • DSL for describing transformations

  • Syntax:

    • Concise MT patterns

    • Canonical form

  • Semantics

    • Well-defined formal semantics

    • Facilitate analysis

  • Support for higher-order transformation: fully modeled language


Quality assessment of mt

Quality assessment of MT

Goal:

Define quality attributes & propose framework

where transformations are guaranteed to satisfy these criteria


Quality criteria identification

Quality Criteria Identification

  • Quantifiable attributes

  • Techniques to measure them

  • Techniques to evaluate transformations


Correctness

Correctness

Degree to which transformation adheres to a set of requirements

  • Evaluated by V&V techniques

  • Key is to make use of traceability links in a transformation

Inspired by ISO 9126


Re usability

Re-usability

Ease of re-using a transformation

  • Modular composition of transformation units, rules, complete transformations

    • Modular transformations (MoTif)

    • Generic transformations (VIATRA)

    • Higher-order transformations (ATL, AToM3)

Inspired by ISO 9126


Efficiency

Efficiency

Relationship between performance of execution & amount of resources used under specific conditions

  • Benchmarking

  • Optimization at implementation level, but also at design level

  • Ability to handle large models and complex transformations (fan-in/out)

Inspired by ISO 9126


Reliability

Reliability

Frequency & criticality of a transformation to behave in an unacceptable manner under permissible operating conditions

  • Security

    • Fault-tolerance techniques

    • Exception handling

  • Usability

    • Ensure invariant properties

Inspired by ISO 9126


Maintainability

Maintainability

Effort needed to modify the transformation to satisfy new requirements or correct deficiencies

  • Model & transformation evolution techniques can be applied

Inspired by ISO 9126


Interoperability

Interoperability

Cooperation between a given model transformation and other systems: transformation models & other software

  • Model composition

  • Conform to a common standard serialization of models for I/O


Quality criteria identification1

Quality Criteria Identification

  • Define quality attributes at coarser level

  • Implement techniques to measure these quality criteria

?


V v of transformation patterns

V&V of transformation patterns

How to verify the MT design patternsagainst the quality attributes?

Model Checker

QualityCriteria

Formal Properties

Result

MT pattern

Domain meta-model


Assisted design of model transformation

ASSISTED DESIGN OF MODEL TRANSFORMATION

Goal:

Reduce negative impact of model transformationin complex projects

  • Deep knowledge of semantics of transformation language

    • Rule scheduling

    • Attribute/constraint specification

    • Control logic


The ultimate model transformation ide

The ultimate Model transformation IDE

  • Detect design patterns based on the pattern catalog during the development of transformations

  • Detect a non-exact match of a cataloged pattern & propose a resolution to make it compatible with catalog


Pattern detection

Pattern Detection

  • Problem: transformations are defined in a declarative way

    • Hampers maintenance tasks

  • Techniques to detect MT pattern in a given MT

    • Stochastic based on design space exploration

    • V&V techniques to statically analyze MT & derive structural/behavioral correspondences with existing pattern

    • Or re-use MT techniques to detect patterns


Hot for detecting patterns

HOT for detecting patterns


Resolution of ill formed design

Resolution of Ill-Formed Design

Goal:

Improve non-functional properties of the transformation

  • Detect design patterns in a given transformation that arealmost similar to one from the catalog


Resolution of ill formed design1

Resolution of Ill-Formed Design

  • Detect non-exact matches

  • Stochastic, search-based techniques to detect similarities between fragments of an MT

    • Search-based

    • Transformation by demonstration

      • Advanced IDE that records examples of how to use a design pattern

  • Evaluation of detection can only be done empirically, by observation


Conclusion

Conclusion

  • Quality Criteria

  • Design Patterns meeting criteria

  • Automated assistance for MT development

    Your suggestions are vital!Topics for afternoon discussion?


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