Business rules do not get the attention they deserve in Software Engineering. They are mostly ignored in the specification phase and implemented in an adhoc manner in the target platform. We discuss why this is not going to work anymore

1. The secret life of rules in Software
Engineering
Jordi Cabot
@softmodeling – jordicabot.com June 2017

2. Disclaimer

4. “As a writer, my job is to change your
perspective and make you think
outside the box.” - Mary Sage Nguyen

5. Model-Driven Software
Engineering: Preliminaries

6. • Grady Booch – history of softwre engineering
The entire history of software engineering is that of
the rise in levels of abstraction
- Grady Booch
Goal: Write less and less code

7. Everybody is doing it

8. MDE is just the logical next step

10. Model
Rapid prototyping
Static analysis
Code generation
Automated testing
Refactoring/
Transformation
Documentation
[Illustration by Bernhard Rumpe]
MDE = Models as key elements in all aspects of
Software Engineering

11. The MDE equation
Models +
Transformations =
Software

12. Original
model
1st
refinement
nth
refinement
Model-to-model
Transformation
Model-to-text
Transformation
...
Requirements
Use Case
Class Diagram
Java Project

13. What is a model
13
City of Nantes = “system” to be
modeled
A map is a model of this system
Its legend is the grammar/
metamodel
Different maps can focus on
different “views” of the city

14. Not so different from languages
MDE Grammarware
MOF
(metametamodel)
UML
(metamodel)
ABank.uml
EBNF.g
Java.g
MyProgram.java

15. MDE benefits
+productivity +quality +Maintenance ….
2X – 8X 1.2X – 4X 80%

16. I’ll skip my over 500
“introductory” slides to
MDE (but they are onine):
http://mdse-book.com

17. 1: Rules in Software Engineering?

18. What is a business rule (for us)?

19. Definition
 Business rule:
 Statement that defines or constrains some
aspects of the business
 Applied on business terms
 Independent from programming languages
Defining Business Rules. "What Are They Really." The Business Rules Group, 2000, 1-77

20. Conceptual Schema Centric Development – Antoni
Olivé
It is both useful and
necessary to integrate
the business rules and
conceptual modeling

21. Rule Modeling: UML+OCL

22. Some first examples

23. Why OCL (Object Constraint Language)?
23
 No serious use of UML
without OCL!!! (limited
expressiveness of ALL
(graphical) notations
To learn OCL see http://modeling-
languages.com/ocl-tutorial/

24. OCL: Basic Concepts
• OCL is a rich language that offers predefined mechanisms for:
– Retrieving the values of an object
– Navigating through a set of related objects,
– Iterating over collections of objects (e.g., forAll, exists, select)
– Expressing conditions on those collections (sum, size,…)
• OCL includes a predefined set of types + operations
– Primitive types: Integer, Real, Boolean and String
– Collection types: Set, Bag, OrderedSet and Sequence
– 3 valued logic

25. OCL examples: Constraints
context Quote inv OverZero: self.value > 0
context BlackListed inv NoRentalsForBlackListed:
self.rental->forAll(r | r.startDate < self.blackListedDate)

26. OCL Example: Derivation Rules
context Customer::discount: integer
derive:
if not self.premium then
if self.rental.car.carGroup->select(c|c.category=’high’)-
>size()>=5
then 15
else 0 endif
else 30 endif

27. OCL Example: Contract
context Rental::newRental(id:Integer, price:Real, startingDate:Date,
endingDate:Date, customer:Customer, carRegNum:String)
pre: customer.licenseExpDate>endingDate
post: Rental.allInstances->one(r | r.oclIsNew() and r.oclIsTypeOf(Rental) and
r.endingDate=endingDate and r.startingDate=startingDate and ….

28. Relationship of OCL with other rule-based
languages? Very little research on this

29. Rule verification for MDE

30. 30
Original
model
1st
refinement
nth
refinement
Model
Transformation
Model
Transformation
Source
Code
...
MDE-based software development process
Errors in models breed and generate
more errors in the code
Why model verification

31. Person
name: string children
2
*
+ rule : Nobody can be his own ancestor
parent

32. ….
.No finite (and non-empty) solution

33. Consistent?
Proven?
Formalism / Logics
• Description Logics (DL)
• High-Order Logics (HOL-OCL)
• Deductive database queries (CQC)
• SAT (Alloy + UML2Alloy), …
Translate
Prove
Deduce
UML model
Class diagram + OCL constraints
Typical solutions

34. MDE
A Grand Challenge

35. Verification
Automation Efficiency
Expressiveness Precision(completeness)
Pick only 2-3

36. Our pragmatic approach: Bounded verification
36
Solution?
Constraint Satisfaction Problem
1. Variables – basic types + struct/list
2. Domains – finite
3. Constraints – Prolog
4. Property  Additional Constraint
Translate
Solve
Deduce
Property?
+
Model Constraints
Cabot, Clarisó, Riera: On the verification of UML/OCL class diagrams
using constraint programming. Journal of Systems and Software 93: 1-23

37. Resolution of the CSP
Define cardinality variables
Constraints on cardinalities
Assign cardinalities
Define attribute variables
Constraints on attributes
Assign attributes
Proof

38. Rule implementation for
MDE

39. Implementing OCL constraints
Triggers in a
database
Views on a
database
If-then
conditions in
Java methods Cabot, Teniente: Incremental integrity checking
of UML/OCL conceptual schemas. Journal of
Systems and Software 82(9):
Key point:
Incremental
checking

40. The BAD News

41. • Very few tools support it
• Even less generate code from OCL
Nobody uses OCL
• No explicit rule engine component
• Mixed with code
• Adhoc approaches are used (errors, incomplete and
inefficient)
Rule implementation is left to programmers

42. It is both useful and
necessary to integrate
the business rules and
conceptual modeling

43. 2: (Future) of Rules in SW Eng

44. Data-ism is the new
religion

45. Big data is not schemaless. At
most, we can say it is “less-
schema” than other data

46. but we cannot count on the persistence
mechanims to control the data

47. SW Eng needs to
adapt
- Rules as first class citizens
- With an explicit representation and
rule extraction mechanisms
- Dedicated engines

48. Example: Citizen
developers on Open Data

49. “the goal of the project is to
make the promise of
open data a reality by
giving non-technical
users tools they can use
to find and compose the
information they need”

50. Open Data for All

51. Web APIs as main citizen

52. Web APIs as main citizen

53. Web APIs as main citizen
… a.k.a. JavaScript Object Notation…
…a text-based open standard
designed for human-readable data
interchange…
…derived from the JavaScript
scripting language for representing
simple data structures and
associative arrays, called objects…
JSON

54. Application Example
S
1
S
2
?
S
1
S
2

56. Our approach
Cánovas, Cabot: JSONDiscoverer: Visualizing the schema lurking behind
JSONdocuments. Knowl.-Based Syst. 103: 52-55 (2016)

66. Many Challenges
• On the data (scope)
• On the quality of the data
• On the precision of the discovery process
• Propagation of uncertainty values
Uncertainty
• How to discover them
• Optimal paths considering non-functional trade-offs (e.g. price vs quality)
Non-functional properties

67. UML
IFML
GraphQL
Schema

69. Cognification of rules

70. Cognification:
The application of knowledge
to boost the performance
and impact of a process

71. During the industrial
revolution, every machine got
an electrified version
The next revolution is the
cognificatin of everything via
cheap access to specialized
AIs

73. Cognification
ML
Crowdsourcing
Ontologies
Big Data
…

74. Cognifying Rules: 5 examples
Rule Modeling bot as virtual assistant
Rule inferencer to discover schema of unstructured data
A code generator that mimicks a company programming
style
A real-time rule reviewer
A morphing rule modeling tool that adapts to the user
profile (language, input format…)

75. 3: We should do it together

76. We (RuleML and
MoDELS
conferences) seem
to live in two
parallel universes

77. Some sad stats (2009 – 2016)
0
2
4
6
8
10
12
RuleML Papers with
UML or OCL in the
title
RuleML Papers with
"Software" in the
title
Researchers
publishing also at
Models
Valerio Cosentino et al: MetaScience: An Holistic Approach for Research Modeling. ER 2016

79. Let’s stop this
madness of
reinventing the
wheel in each
community

80. BUT NOT EASY
- Publishing as a newcomer
- Adapt vocabulary & style
- Adapt technologies (API vs
Semantic Web, DL vs OCL,…)

83. http://modeling-languages.com

84. Let’s work
together
jordi.cabot@
icrea.cat
@softmodeling