This paper presents a new technique to automatically generate a diverse set of instantiations from a conceptual schema. It strengthens integrity constraints in a structured way to produce classifying terms that partition the solution space. A model finder then computes one instantiation from each equivalence class to maximize diversity. Future work includes prioritizing constraints, optimally selecting classifying terms, and evaluating on large case studies.

1. A Systematic Approach to Generate
Diverse Instantiations for Conceptual Schemas
Loli Burgueño, Jordi Cabot, Robert Clarisó, Martin Gogolla
ER’19
Salvador, Brazil, November 7th, 2019

2. Introduction
• typically require the creation of instantiations of the conceptual schema
• i.e., examples for an information base of a conceptual schema
• used as:
• Illustrations (for simulation, etc.)
• Counterexamples (describe invalid configurations, etc.)
• Test cases (to capture scenarios that should be checked, etc.)
2
Verification, validation and testing to ensure the
quality
of a conceptual schema

3. Motivation
• Cover a broad spectrum of different configurations and scenarios
• Otherwise, relevant corner cases might be missed
• causing faults and/or wrong conclusions
3
The set of instantiations should be diverse

4. Motivation
4
Model finders: Automatically compute (valid)
instantiations satisfying all constraints
• Domain expert who creates instantiations
manually?
 very time-consuming and is usually not feasible

5. Motivation
5
While model finders automate the generation process,
they do not guarantee diverse solutions
• Domain expert who creates instantiations
manually?
 very time-consuming and is usually not feasible

6. Our proposal
• New technique for generating test cases
(object models) for UML class models
• Guide construction process through classifying terms
• Classifying term:
• properties that can be used to partition the solution space
(OCL expressions on the class model)
6

7. Our proposal
• Classifying terms as a means of Equivalence Partitioning
• From each equivalence class one representative
object model is chosen
• Test cases significantly different with w.r.t. classifying
term
7

8. Classifying Terms
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-- CT hasChildren:
Person.allInstances->forAll( p |
p.child->isEmpty())
Two equivalence classes!
hasChildren=True
hasChildren=False
Model space

9. Classifying Terms
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Four equivalence classes!
-- CT yearBDefined:
Person.allInstances->forAll( p |
p.yearB.oclIsUndefined())
-- CT hasChildren:
Person.allInstances->forAll( p |
p.child->isEmpty())
Model space
hasChildren=F
yearBDefined=F
hasChildren=T
yearBDefined=T
hasChildren=F
yearBDefined=T
hasChildren=T
yearBDefined=F

10. Classifying Terms
• Tool context:
USE & its model
validator
• Example:
wGp withGrandparent
w2c with2children
w2p with2parents
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11. Our Proposal
We observed that classifying terms are typically related to the
integrity constraints in the schema
If we mutate the schema's integrity constraints in a structured
way, we can automatically generate classifying terms
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Proposing suitable classifying
terms requires domain knowledge

12. Our Proposal
Mutation = Strengthening of integrity constraints (OCL)
i.e., generation of a more restrictive version
Strengthening – Example:
expL or expR  expL and expR
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exp exp+
exp
exp+

13. Mutation = Strengthening of integrity constraints (OCL)
i.e., generation of a more restrictive version
Strengthening – Further examples of mutation operations:
Our Proposal
13

14. Our Proposal
• Meaningful CTs
• border cases become clear
• For example, one instantiation where a particular constraint holds and
another one where the same constraint fails
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15. Running Example
15

16. • Two generated instantiations
Running Example
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17. • USE tool (1)
• Implemented in Java
• GUI
• packages to load, modify
and inspect models and
instantiations
• commands to invoke the
KodKod relational solver
for model finding
Tool Support
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(1) https://sourceforge.net/projects/useocl/

18. • Process:
1. Candidate generation:
a. Each OCL integrity constraint is strengthen by means of a post-order traversal of
its AST
b. For each expression in the AST, we generate the candidate strengthenings by
combining the strengthenings of its subexpressions
2. Candidate selection:
a. Can be performed in different ways: randomly; using heuristics or with the help of
a domain expert
3. Computation of instantiations:
a. we provide the CTs to the model finder
b. it finds one instantiation in each of the equivalence classes induced by the
partition
Process
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• Input: a UML class diagram annotated with OCL
invariants (.use file)

19. 19
Discussion
• Performance
• In the generation of the CTs, it is negligible (151 ms for running example)
• Computing the instantiation is always a bottleneck but we significantly
reduce number of times we trigger the generation  improving the
overall computing time
• Correctness and completeness
• Any object model satisfies all constraints
• Support for all OCL 2.4 except for ordered collections and recursively defined queries
• Generation of boolean CTs. Integer CTs in the future

20. Discussion
• Combination strategies for classifying terms
• We could change the way in which the equivalent
classes are traversed in the solution generation
process
• For instance:
1. focus first on one CT alone and generate diverse examples
(i.e. emphasizing local diversity )
2. combine all classifying terms and generate solutions that
explore equivalent classes taking into account the value of
different classifying terms at the same time (i.e.
emphasizing global diversity )
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21. New approach to enforce the generation of a diverse set of
instantiations from a given conceptual schema
Conclusions & Future work
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• Prioritize constraints which are potentially more useful
• Definition of strategies to optimally select and combine different CTs
and guide the exploration of model solutions for each combination.
• Large case studies

22. A Systematic Approach to Generate
Diverse Instantiations for Conceptual Schemas
Loli Burgueño, Jordi Cabot, Robert Clarisó, Martin Gogolla
ER’19
Salvador, Brazil, November 7th, 2019