Budapest University of Technology and EconomicsDepartment of Measurement and Information Systems

Quick fix generation for DSMLs

Ábel Hegedüs, Ákos Horváth, István Ráth, Moisés Castelo Branco*, Dániel Varró

Budapest University of Technology and Economics*University of Waterloo

VL/HCC 2011Pittsburgh, 2011.09.19.

2

Source code editing and fixing errorsVisual Studio Smart Compile Auto CorrectionNetbeans Editor HintsEclipse Quick Fix

3

Development support in programming languages

Economy classo Syntax-highlighting,

lightweight validation Business class

o Autocompletion, semantic validation

First-classo Quick fixes, refactoring

Examples:Custom DSL

Standard DSL (e.g. SQL)

Java, C#, PHPEclipse-like IDEs

4

Development support in modeling languages

Economy classo Syntax-driven

editing Business class

o Validation

First-classo Quick fixes,

refactoring

Examples:Traditional DSM,COTS modeling tools

UML toolsModern DSM

?

Need for generic approach for fix generation:• Language-independent• Customisable• Efficient

5

Domain Specific Modeling Languages Ubiquitous in software development

o Requirement specificationo System analysis and verificationo Code generationo Business processeso Standard DSMLs e.g.

• Matlab Simulink• UML profiles• BPEL, BPMN

o Custom DSMLs Sophisticated DSML

development toolso Eclipse Modeling Frameworko Microsoft DSL Toolso MetaEdit

6

Domain-specific constraints Syntax-driven editing is not enough!

o Design errors, company policies, best practices Additional inconsistency rules

oWell-formedness constraintso Structural correctness

Syntactically correct process

Violates basic process modeling practices

7

Example: BPMN process validationHow to fix these inconsistencies?

StandardEclipse BPMN tool

8

Requirementso Domain-specific

• Inconsistency rules• Editing operations

o Problem-specific

Output

Essence of the approacho Design Space Exploration

Quick fix generation for DSMLsGraph patterns and pattern matching

End event start of edge

Non-messaging event receiving message

9

Quick fix

Requirementso Domain-specific

• Inconsistency rules• Editing operations

o Problem-specific• Input model• Violation context

Outputo Sequence of applicable editing operationso Eliminate violations in the selected context

Essence of the approacho Design Space Exploration

Modify target of sequence edge

Pre

Post

Quick fix generation for DSMLsGraph patterns and pattern matching

Graph transformation

10

Design Space Exploration (DSE) High-level overview

Initial (inconsistent) model

Modified model

Operation

Partially corrected model

Violations identified by inconsistency rules

Application of a syntax-driven editing operation

Violations fixed on selected context

Termination condition:• number of solutions• time limit• depth

11

Modified model

Operation

Initial (inconsistent) model

Design Space Exploration (DSE) High-level overview

Operation sequence =

quick fix

Efficient solution detection• violations evaluated

incrementally

Exploration guided by• heuristics• already visited states

Efficient application of operations• Graph

TransformationEfficient backtracking of operations

Partially corrected model

12

Quick demonstration: BPMN quick fix

13

Presentation of quick fixes

List of fixes Sequence of operations Operation name Context

14

Case studies and evaluation Main evaluation criteria

o „On-the-fly” generation of quick fixeso Representative set of• Inconsistency rules• Editing operations• Input models and violations

Process size and complexity representative to typical BPMN processes and simpler DSML models

Real-life processes from the financial domaino Centralized customer registeringo Corporate procurement

15

Evaluation results

„On-the-fly” quick fix generation works in many cases Finding multiple solutions can increase runtime significantly

egyéb részletek

Memory:50-200 MBVisited states:50-40000

Logarithmic scale1st: 5ms2nd: 13ms3rd: 271ms

1 sec vs 160 secTotal:1 sec vs 160 sec

16

Evaluation results Identified significant factors

o Number of local violationso Operation complexityo Solution density

Usable during editing for fixing local errorso 12 inconsistency ruleso 16 editing operations (create,modify,delete)o 1-3 local violation, at most 2 step fix

17

Future work Knowledgebase-driven DSE

o User-aided fix generationo Learn applied fixes for future use• Store quick fix as a special editing operation

Improved search strategyo Partial order reductiono Better heuristics

Resumable search Streamlined integration with EMF tools

o Ease adaptation to third-party software

18

Summary Novel approach

o for an important language engineering problemo applicable to graph-based modeling languages

Problem formalized and solved efficientlyo using graph transformations, incremental pattern matching and

design space exploration Prototype implementation

o Evaluated on BPMNo Also presented at the Demo session (tonight)

We would like to thank the Bank of the Northeast of Brazil (Banco do Nordeste – BNB) for providing the case studies.

Thank you! Contact: hegedusa@mit.bme.hu

References

Fault Tolerant Systems Research GroupBudapest University of Technology and Economics

inf.mit.bme.hu

VIATRA2 Model Transformation Framework

viatra.inf.mit.bme.hu

EMF IncQuery

viatra.inf.mit.bme.hu/incquery

Generative Software Development LabUniversity of Waterloo

gsd.uwaterloo.ca