A Model Driven Reverse Engineering framework for extracting business rules out of a Java application

A Model Driven Reverse Engineering framework for extracting business

rules out of a Java application

Valerio Cosentino - IBM, AtlanMod, INRIA & EMN, Nantes - valerio.cosentino@fr.ibm.comJordi Cabot - AtlanMod, INRIA & EMN, Nantes - jordi.cabot@mines-nantes.frPatrick Albert – IBM - albertpa@fr.ibm.comPhilippe Bauquel – IBM - bauquel.p@fr.ibm.comJacques Perronnet – IBM - jacques_perronnet@fr.ibm.com

RuleML 2012, Montpellier, France – 29 August

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

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 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

Transcript of A Model Driven Reverse Engineering framework for extracting business rules out of a Java application

PowerPoint Presentationprimach-tech.com/wp-content/uploads/pm160v2.pdf · installation and commissioning could be completed in ... - Extracting belt conveyor driven by frequency inverter,

PD-L1 driven Tolerance Protects Neurogenin3 …...PD-L1 driven Tolerance Protects Neurogenin3-induced Islet Neogenesis to Reverse Established Type 1 Diabetes in NOD Mice Rongying Li

Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures · 2014-02-24 · Epitome driven 3-D Diffusion Tensor image segmentation: on extracting speciﬁc

A Reverse Engineering Methodology for Extracting ...

A data-synthesis-driven method for detecting and …sgao/papers/2016_IJGIS_DataDriven...A data-synthesis-driven method for detecting and extracting vague cognitive regions Song Gao

EXTRACTING SEMANTICS FROM LEGACY SOURCESufdcimages.uflib.ufl.edu/UF/E0/00/12/10/00001/topsakal_o.pdf · EXTRACTING SEMANTICS FROM LEGACY SOURCES USING REVERSE ENGINEERING OF JAVA

Proceedings of the 1st International Workshop on REVERSE ... › km › retr › camera › retr05.pdf · Reverse engineering aims at extracting many kinds of information from existing

JF010/11E CVT Manual...This CVT consists of a Torque Converter, Forward Clutch, Reverse clutch, Planetary Gear Set for Reverse, Drive Variator (pulley), Driven Variator (pulley) and

MoDisco: a Model Driven Reverse Engineering Framework

XIS-Reverse: A Model-Driven Reverse Engineering Approach for …isg.inesc-id.pt/alb/static/papers/2017/c139-ar-MODEL... · 2017. 1. 5. · The conﬁguration panel of the tool has

Reverse and Forward Transformation Chain - summersoc.eu · Hugo Brunelière, Jordi Cabot, Grégoire Dupé, Frédéric Madiot: MoDisco: A model driven reverse engineering framework.

Foundations of Model (Driven) (Reverse) Engineering - CiteSeer

Foundations of Model(Driven)(Reverse)Engineering …...Foundations of Model(Driven)(Reverse)Engineering : Models Episode I: Stories of The Fidus Papyrus and of The Solarus1 Jean-Marie

ReverseCodeEngineeringfiles.meetup.com/7108012/Reverse Code Engineering.pdf · Introduction! “Reverse!engineering &is&the&process&of&extracting&the& knowledge&ordesign&blueprints&from&anything&manH

Reverse Bayesianism: A Choice-Based Theory of Growing ... · The reverse Bayesianism aspect of our approach is driven by axioms that have the flavor of Savage’s (1954) sure thing

Perfumes Extracting

Solar-driven desalination with reverse osmosis: the state of the art

A Model Driven Reverse Engineering Framework for Extracting

Reverse Engineering of Pump Casing and Impeller · Reverse Engineering in general is the process of extracting knowledge or design information. This may often involve disassembling

J. Bezivin, Principles and Applications of Model Driven ... · Principles and Applications of Model Driven Engineering ... driven reverse engineering, stream-based and data-centric