The document discusses using the IRIS tool to extract business rules from source code and detect duplicated rules. It outlines extracting terms and conditions from database-related code elements. Rules are extracted by identifying top-level conditional statements and associated code elements. Duplicated rules are detected by normalizing rules and comparing identifiers. Examples show rules extracted from COBOL code and duplicated rules identified. Future work includes refining detection of similar rules and removing irrelevant elements.

1. Extracting Business
Rules and Removing
Duplication With IRIS
Wei Wu, École Ploytechnique de Montéal
Stéphane Vaucher, Benchmark Consulting

2. Outline
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Business Rule (BR)
Problem
Previous Works
BR Extraction
Detect Duplication
Examples
TODO

3. Business Rule
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Business rules (BRs) are statements that prevent,
cause, or suggest business activities to happen.
- The GUIDE Business Rules Project
Example:
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If the driver age under 25, car rent rate is $80 per
day, otherwise, it is $60 per day.

4. Business Rule
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OMG Semantics of Business Vocabulary and
Business Rules (SBVR)

5. Conceptual Model

6. Business Rule
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Rule: If the driver age under 25, car rent rate is
$80 per day, otherwise, it is $60 per day.
Term:
Driver age
 Car rent rate
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Fact:
The driver age under 25
 Set car rent rate to $80
 Set car rent rate to $60
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7. Problem
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Written by business analysts
Implemented in business systems
Documents and implementations are not well
synchronized
Actual BRs are only in the source code
No reliable way to extract them

8. Previous Works
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Erik Putrycz and Anatol W. Kark, Recovering Business Rules from Legacy
Source Code, the Proceedings of The International RuleML Symposium on
Rule Interchange and Applications (RuleML-2007), 2007
Harry M. Sneed, Extracting Business Logic from existing COBOL programs
as a basis for Redevelopment, Proceedings of the 9th International Workshop
on Program Comprehension (IWPC’01), 2001
Huang et al., Business Rule Extraction from Legacy Code, COMPSAC '96
Proceedings of the 20th Conference on Computer Software and Applications,
1996
Harry M. Sneed and Katalin Erdos, Extracting Business Rules from Source
Code, WPC '96 Proceedings of the 4th International Workshop on Program
Comprehension, 1996
Softwareminingʹs BRE Toolkit,
http://www.softwaremining.com/services/Business_Rule_Extraction.jsp

9. Limitations
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Not KDM based - Language specific
Not distinguish business and non-business
variable.
Need manually-identified variable-of-interest
Not handle duplication

10. Term Unit Extraction
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DB Related DataElements
ColumnSet – ItemUnit
 ColumnSet – InDataRelations – DataModel –
DataElements
 DataModel – DataAction - DataElement
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DB Related DataElements - TermUnit

11. Identify DB-related Actions
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ActionElements
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InReads and InWrites of DB Related DataElements
Top-level Conditions
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if(no-db-realted-Data){
if(db-related-data1){
if(db-related-data2){
}
}
if(db-related-data3){
}
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}

12. Identify Top-level Condition
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Condition not dominated by any condition which
accesses DB-realted DataElement
To detect not-top-level conditions
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Get basic-block (BB) of each condition
Get all the dominators of the BB
Get the InFlow of the first ActionElement of each dominator
If the InFlow is TrueFlow or FalseFlow and the condition of
the Flows accesses some DB related DataElement

13. RuleUnit Extraction 2-1
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Create RuleUnit for the TrueFlow (FlaseFlow)
of each top-level condition
Put the condition to the implementation list of
the RuleUnit
Create a FactUnit for the condition
Create a ConceptualRole for the FactUnit and
put it to the ConceptualElement list of the Rule
Unit

14. RuleUnit Extraction 2-2
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Create a FactUnit for each ActionElement in the
branch
Create a ConceptualRole for the FactUnit and put it to
the ConceptualElement list of the Rule Unit
Create a normalized ID for the RuleUnit based on all
its ConceptualRoles
If there’s another RuleUnit with the same ID, just add
the implementation list to that RuleUnit.
Otherwise, add the RuleUnit to the Conceptual Model

15. ActionElement Normalization
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Intermediate Presentation
Left - OutReads
 Operator - Type
 Right – OutReads
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Conditions
NNF
 DNF
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16. ActionElement Normalization
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Alphabetical Normalization
b == a
->
 b<a
->
 d>c && b<a ->
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a == b
a>b
a>b && c<d

17. Examples
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BR:
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121810
121810
121810
121810
121810
012211
121810
121810
121810
121810
121810
012211
121810
121810
121810
121810
121810
121810
121810
121810
EVALUATE SQLSTATE
02443500
WHEN SQL-OK
02443600
IF WORK-HV-TXL-CLS-WASH-SALE-AMT >= 0
02443700
IF WORK-HV-TXL-CLS-RECORD-DEL-SW = 'N' AND 02443800
TAXL-TRANS-TYPE-NO = 930
02443900
PERFORM RECALCULATE-AVG-WASH-QTY
02444000
SET PROCESS-AVG-WASH
02444100
TO TRUE
02444200
ELSE
02444300
IF WORK-HV-TXL-CLS-RECORD-DEL-SW = 'Y' AND 02444400
(TAXL-TRANS-TYPE-NO = 500 OR 600)
02444500
PERFORM RECALCULATE-AVG-WASH-QTY
02444600
SET PROCESS-AVG-WASH
02444700
TO TRUE
02444800
END-IF
02444900
END-IF
02445000
END-IF
02445100
WHEN OTHER
02445200
PERFORM ERROR-AVG-WASH
02445300
END-EVALUATE.

18. Examples
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Duplicated
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080306
091906
091906
080306
080306
IF HV-TXL-MST-OPEN-DATE IS LESS THAN
00862200
WORK-TAXL-UPDT-DIV-EX-DT-DB2 OR 00862300
HV-TXL-MST-OPEN-DATE IS EQUAL TO DF-NINES-DATE
00862400
PERFORM UPDATE-LIQUIDATION-PAYMENT
00862500
END-IF
00862600
IF TAXL-QTY IS NOT EQUAL TO ZERO
00862700
PERFORM FETCH-ROW-TXL-OPEN-FIFO
00862800
END-IF
080306
091906
091906
080306
080306
IF HV-TXL-MST-OPEN-DATE IS LESS THAN
00869400
WORK-TAXL-UPDT-DIV-EX-DT-DB2 OR 00869500
HV-TXL-MST-OPEN-DATE IS EQUAL TO DF-NINES-DATE
00869600
PERFORM UPDATE-LIQUIDATION-PAYMENT
00869700
END-IF
00869800
IF TAXL-QTY IS NOT EQUAL TO ZERO
00869900
PERFORM FETCH-ROW-TXL-OPEN-FIFO
00870000
END-IF
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19. Examples
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Data ActionElements: 2,084
Code ActionElements: 47,699
IF conditions: 2,724
DB related ActionElements: 12,195
DB related IF conditions is 621
Top-level if conditions is 591
ActionElements in BRs are 2,038

20. Ongoing Works
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Compute the percentage of the ActionElements
involved in BRs
Compare with using graph matching techniques
to identify duplicated or similar BRs

21. Future Works
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Get feedback from business people
Multi-objective way to detect duplicated or
similar BRs
Peephole Optimization
Remove irrelevant ActionElements
(Usedef/Defuse)
Inter-procedural analysis

22. Thank You!
Extracting Business
Rules and Removing
Duplications With IRIS