a model driven reverse engineering framework for extracting business rules out of a java application

1

© 2009 IBM Corporation

A Model Driven Reverse Engineering framework for extracting business

rules out of a Java application

Valerio Cosentino - IBM, AtlanMod, INRIA & EMN, Nantes - [email protected] Cabot - AtlanMod, INRIA & EMN, Nantes - [email protected] Albert – IBM - [email protected] Bauquel – IBM - [email protected] Perronnet – IBM - [email protected]

RuleML 2012, Montpellier, France – 29 August

2 © 2009 IBM Corporation

Outline

Introduction– Context & problem– Example– Business rule extraction process

Framework overview– Model discovery– Variable classification– Business rule identification– Business rule representation

Conclusion & Future work


Introduction - context & problem

Context: every organization needs to periodically reevaluate and evolve its company policies enforced in its Information System (IS) by means of a set of business rules




Business rule: – « Relevant action aiming at constraining some precise aspect of a

business »– Key component for ISs






Problem: policies and rules must be aligned at all time, but in most of ISs business rules are scattered among the source code.







Hard to find the business rules within the IS even for small application







Hard to find the business rules within the IS even for small applicationHard to evolve (quickly and safely) company policies


Introduction - example

We use as running example a small application representing the predator-prey problem. The application simulates the behaviour of animals (foxes, rabbits, birds) and humans (hunters) in a meadow. Each actor animal or human can act and move according to its nature.Two different functionalities are implemented in this application: one represents the business logic and describes how predator-prey interactions affect the population size. The second one is used to store statistical information about the actors participating in the simulation



Rules modeling the application: – Hunters:

Never die Hunt animals

– Rabbits & Birds: Can die by being eaten by foxes, hunted by hunters, of

starvation, old age or overcrowding Can breed when they reach their breeding age Eat grass

– Foxes: Can die by being eaten by hunters, of starvation, old age or

overcrowding Can breed when they reach their breeding age Eat rabbits and birds


Introduction – example rules are scattered in the source code

old age

starvation



rabbit killed by a fox



rabbit killed by a hunter



overcrowding


Introduction - business rule extraction process

Business rule extraction (BREX) process: – Allows extracting business rules out of an IS, isolating the code

segments which are directly related to business– Three major activities:

Variable Classification → finds variables related to domain/business concepts and hintining at BRs

Business rule identification → collects chunks of code related to the variables identified in the previous step

Business rule representation → presents the extracted BRs by means of artifacts (graphs, textual representations, …)


Introduction - business rule extraction process

Business rule extraction (BREX) process: – Allows extracting business rules out of an IS, isolating the code

segments which are directly related to business– Three major activities:

Variable Classification → finds variables related to domain/business concepts and hintining at BRs

Business rule identification → collects chunks of code related to the variables identified in the previous step

Business rule representation → presents the extracted BRs by means of artifacts (graphs, textual representations, …)

Model Driven Engineering techniques:– Abstract & homogeneous representation– Modular solving process– Non-intrusive solution


Framework overview - model discovery

A new operation (Model Discovery) is added to the BRE process to move the problem from a grammarware technological space to the modelware one.– Input: source code– Output: platform specific model (PSM)

Modisco discovery component(http://www.eclipse.org/MoDisco/)


Framework overview - variable classification

Variables Classification identifies the domain variables together with their containing classes – Input: PSM– Output: model containing all domain's classes and their inner

variables


Framework overview - variable classificationeach link represents a type dependency.ex.: the link connecting Simulator and AnimatedView means that the class Simulator uses the type AnimatedView


Framework overview - variable classificationthe algorithm starts from the classes

containing a graphical import (awt, swing, ..)



for each of those classes, a set of connected classes is calculated (recursively)


Framework overview - variable classificationthe green set (business domain) contains the classes representing the logic modelling how

the actors act/interact each other


Framework overview - variable classificationfor the same application we may find several

business domains (in blue the classes related to the stats of the simulation)


Framework overview - variable classification - metamodel

For each class in a group, its variables are classified in:– Single-access: class attributes occurring at most once on the left

side of an assignment– Multi-access: class attributes occurring more than once on the left

side of an assignment– Potentials: variables declarated in methods and occurring on the left

side of an assignment


Framework overview - variable classification - metamodel

For each class in a group, its variables are classified in:– Single-access: class attributes occurring at most once on the left

side of an assignment– Multi-access: class attributes occurring more than once on the left

side of an assignment– Potentials: variables declarated in methods and occurring on the left

side of an assignment

– Traceability: relates the classified variables to the source code


Framework overview - business rule identification



Domain model extraction:– Input: PSM, the domain variables model– Output:

Model conforming to the Business Object Model/Vocabulary [BOM/VOC] metamodel of IBM WebSphere ILOG Jrules

– Extracts method signatures and class attributes from the classes containing the domain variables identified in the variables classification step

– Provides a default vocabulary for these entities to be reused in the description of the business rules

– The user can tune the process and define its verbalization



Domain model extraction:



Slicing operation:– Input: PSM, a variable i contained in the domain variables model– Output:

PSM enriched with annotations (PSMA) on all the statements, variable declarations and methods relevant for i




PSM enriched with annotations (PSMA) on all the statements, variable declarations and methods relevant for i (ex: alive)





A statement is marked as « Rule » if it modifies the value of the variable i

Alive is the name of the variable we are slicing

Id to identify each business rule

The granularity index is the distance of a statement from the statement that modifies the value of the variable i





A method is marked as «Related» if it contains a rule statement





A statement containing a method invocation that allows reaching the statement that modifies the value of the variable i is annoted as «Rule»



A variable declaration is marked as «RELATED-VARIABLE» if it is used inside a «Related» or «Rule» statement.


Framework overview - business rule identificationA method is annotated as «Reachable» if one of its invocations occurs in a «Related» statement or in another «Reachable» method.



This attribute defines the distance from the method containing the «Rule» statement



the slicing generates annotations even for the classes outside the business domain



Business rules model extraction:

once the slicing operation is over, two options are proposed:1 - regenerating the application putting as comments the slicing annotations2 – executing the business rules model extraction, reducing the slicing information just for the classes inside the business domain (green or blue set)



Business rules model extraction:– Input: Domain model and PSM enriched with annotations (PSMA) on

all the statements, variable declarations and methods relevant for i– Output:

Business rule model for the variable i

– PSMA contains information of classes outside the business domain. The domain model is used to exclude them



Classes related to the variable alive:



Classes outside the domain:



Classes inside the domain:



Intersection with the domain classes:


Framework overview - business rule identification - metamodel

Structure represents the statements annotated as

« Related »

following and follower represent the granularity value

RelatedVariable represents Variable Declaration

annotated as « RELATED-VARIABLE »

ReachableMethod represents methods annotated as

« Reachable »Action represents the

statements annotated as « Rule »


Framework overview - business rule identification - metamodelall the elements of this metamodel are Traces, that means that for each of them we know exactly the relative Java source code element.In this way we implement the traceability of the extracted business rules


Framework overview

Business rules representation:

Business rules representation provides human-understandable artifacts (text and graph) for the extracted BRs.– Input: domain model (optional), business rule model-i– Output: text or graph

2 kind of representation are provided (text or graph) and for each of them the domain model vocabulary can be used


Framework overview

Text:

text representation without model domain vocabulary information


Framework overview

Graph:

graph representation with model domain vocabulary information


Conclusion & Future work

MDE benefits:– Non-intrusive approach– Modular framework– Internal/external representation of BRs– Traceability

Test on a real use case:– IBM Rational Programming Patterns :

> 5000 classes, 476 system variables Variable classification step improved with new heuristics Optimization of the slicing operation

Future works:– Extend the framework to other languages– Identify BRs in the other layers composing an application

– Rational Programming Patterns :


Questions

a model driven reverse engineering framework for extracting business rules out of a java application

Technology

business rules business

ibm corporation

ofiss business rules

extracting business

business logic

business rules3

introduction example

introduction example