Sdart AI software developement tutorial

SETN 2010 -‐ TUTORIAL

The so4ware management and engineering in the AI-‐oriented projects

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The so'ware management and engineering in the AI-‐oriented projects

www.SDART.co.uk



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Plan of presenta:on

§ IntroducEon

§ So4ware project management

§ So4ware design

§ AI implementaEon

§ Examples of AI so4ware



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Introduc:on



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Ar:ficial Intelligence

From the so4ware perspecEve:

§ Ar:ficial intelligence = research = high risk

Therefore dealing with AI in so4ware project we

must be very careful and select the proper

methods, technologies and tools.



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Project success

Success

Budget

Time Quality



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Research oriented so'ware

§ Time and budget aren’t usually fixed – it is

possible to extend the research

§ Resources are flexible

§ People mainly have the personal interests (fame,

dissertaEons, publicaEons …)

§ Quality (funcEonality) is the main concern



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Product oriented so'ware

§ Time and budget are usually fixed

§ Quality is adjusted to the available budget and schedule

§ People mainly have the business interests

§ Resources aren’t flexible



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AI & so'ware in projects

AI So4ware

AI So4ware

Proper management

Struggle



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This tutorial Reasons to propose this tutorial:

• There exist the so4ware projects without AI but I’m not

sure if there exist AI projects without the so4ware

(embedded count too!) …

• The knowledge about the so4ware management is

unpopular among AI researchers

• At SDART we deal with the AI-‐oriented so4ware projects



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This tutorial

• Will: •  present a selecEon of the popular techniques and methods for so4ware

development

•  evaluate them from AI perspecEve

•  propose what could be used

§ Will not: •  say what have to be used

•  be a guide ready to be applied step-‐by-‐step



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So'ware project management



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AI & so' – PM nightmare

§ In the project management two types of projects

are the symbols of unpredictability:

§  Research projects

§  …. so4ware projects

§ What could we do dealing with an AI-‐oriented

so4ware projects? Professionally manage them!



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So'ware development schema

Planning

Analysis

Design Implementa:on & coding

Deployment & manitenance



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DM management

§ Data mining management seems to be more

organised than AI

§ There are different models of DM organisaEon:

§ CRISP-‐DM

§ SEMMA

§ DMAIC/DMADV



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CRISP-‐DM Business understanding

Data understanding

Data preparaEon

Modeling

EvaluaEon

Deployment



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SEMMA Search

Explore

Modify

Model

Assess



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DMAIC&DMADV

Define

Measure

Analyse

Improve

Control

Define

Measure

Analyse

Design

Verify



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Management model

§ Can’t we use any DM model to properly manage

the AI&so4ware projects? Yes we could …. only if

the so4ware does not consEtute any significant

part of the project. Meanwhile …

§ AI projects require so4ware to research

§ These projects require so4ware to use AI



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Common risk sources

•  Too opEmisEc

•  Forgot tasks

•  Unavailable •  Too low

•  Bugs •  Increased demands

•  Unskilled •  Leave team

People Technology

Scope & Schedule Budget



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Risk strategies

§ Avoidance

§ RetenEon

§ Sharing

§ ReducEon



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So'ware quality There are many so4ware quality factors, among

them: Efficiency

Security

Consistency

Testability Reliability

Maintability

Usability

Quality



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So'ware quality These factors could be divided into 2 groups

relaEng to:

• users

• developers

So'ware = binaries + code + documenta:on



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So'ware quality If an applicaEon is a product users perceive only

its „front” parts:

• GUI

• Errors

• DocumentaEon



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So'ware metrics

§ Tom DeMarco said „You can’t control what you

can’t measure”

§ So4ware metrics measure some properEes of a

so4ware or its specificaEon e.g. complexity,

quality, efficiency or size



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So'ware es:ma:on

§ EsEmaEon problem

EsEmaEon

Analogy WBS Size-‐based Group Parametric CombinaEon

• Easy

Known • Hard

Known that unknown • Very hard

Unknown that unknow

I think I’ve

forgonen

something, but

what was it???



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Methodology & Life cycle

§ So4ware methodology defines the structure,

plan and management of developed so4ware

§ Methodology contains development philosophy

and tools, methods and models supporEng the

process

§ Life cycle model defines the phases and acEviEes



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M&LC– staged delivery

Requirements

Design

Implementation

Verification

Maintenance



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M&LC -‐ spiral

Spiral model – Boehm 1988



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M&LC – staged delivery Conception

Analysis

Desing

Stage 1

Stage 2

Stage n



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M&LC – design to schedule Conception

Analysis

Desing

Stage 1

Stage 2

Stage n

Software release

Time? Costs?



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M&LC -‐ Agile

The Agile development is based on the iteraEve model

Ini:al planning

Planning

Requirements Analysis and design

Implementa:on

Deployment

Tes:ng Evalua:on



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M&LC -‐ XP

Extreme programming is an Agile implementaEon:

§ Programming in pairs

§ Extensive code review

§ Avoids the programming required in the future

§ Automated tasks

§ Nothing is fixed i.e. everything could be redone



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M&LC -‐ SCRUM

§ Project is being developed by Pigs & Chickens

§ Pig is commined to the project i.e. ScrumMaster,

Team, ProductOwner

§ Chicken is involved i.e.: Stakeholder, Manager



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M&LC -‐ SCRUM

The essenEal part of Scrum are the meeEngs: § Daily Scrum – short Eme (approx. 15 minutes), daily report, all people take part § Scrum of scrums – daily meeEng, a4er daily scrum, limited audience § Sprint Planning Mee:ng – begins each sprint cycle

& Retrospec:ve – end each sprint cycle



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M&LC – Unified Process

§ Is an iteraEve so4ware development framework

IncepEon ElaboraEon ConstrucEon TransiEon

UP ObjecEves

Use Case Driven

IteraEve Architecture centric

Risk focused



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M&LC – UP versions

§ RaEonal Unifed Process

§ Agile Unified Process

§ Open Unified Process

§ Enterprise Unified Process

RUP Six best

pracEces

Develop iteraEvely

Verify quality

Manage requirements

Model visually

Control changes

Use components



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M&LC– selec:on The factors that influences life cycle selecEon are:

§ Size of project § Understood requirements

§ Desired reliability

§ Allowed overheads § Progress visibility

§ SophisEcated management

§ Allowed correcEons



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M&LC -‐ summary

§ Waterfall is sEll the most frequently used model by

the biggest so4ware companies

§ But they do it preparing „yet another version of X”

§ AI projects are usually new for you as well as for

everyone else, so do not expect it to be as easy as

so4ware menEoned above



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Collabora:on

Leader

Member

Member

Member

Member Leader

Member

Member

Member

Member

Homogeneous research team Heterogeneous so4ware team



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Project controlling

Project controlling requires clear definiEon of the

responsibiliEes:

§ Who, what, whom and when

§ Needs reports, working documents,

communicates, standards, plans or charts



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So'ware design



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Architecture

§ Architecture is a high level planning for the so4ware that does not involve any funcEonal

decisions

Requriements Architecture Design Code



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Views The views are perspecEves that are used to represent different aspect of the system:

§ Code/module view

§ Concurrency/process/thread view § Data view

§ Development/structural view

§ FuncEonal/logic view § Physical/deployment view

§ User acEon/feedback view



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Frameworks So4ware architecture is defined by various frameworks defining its necessary elements:

§ 4+1 § RM-‐ODP

§ DODAF

§ SOMF

§ …



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Frameworks -‐ 4+1

4+1

Process view

Development view

Physical view Scenarios

Logical view



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Design concepts

§ AbstracEon & refinement – generalisaEon

§ Modularity

§ Hierarchy

§ InformaEon & funcEonality enclosure

§ Structural parEEoning – verEcal & horizontal



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UML

Unified Modeling Language:

§ Object oriented

§ Unified Booch, OOSE, OMT modelling methods

§ Currently in 2.2 version – the most of so4ware

supports 2.0



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UML Diagrams

UML

Structure

Class Component Composite Structure Package Object Deployment

Behaviour

InteracEon

Sequence CommunicaEon

InteracEon overview Timing

Use Case AcEvity State Machine



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UML Advantages

UML became an industry standard due to:

§ VisualisaEon

§ DocumenEng

§ Specifying

§ ConstrucEng



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UML Tools § Papyrus – hnp://www.papyrusuml.org

§ Poseidon for UML -‐ hnp://www.gentleware.com

§ Visual Paradigm for UML -‐ hnp://www.visual-‐

paradigm.com

§ Umbrello UML Modeller – hnp://uml.sourceforge.net

§ StarUML – hnp://staruml.sourceforge.net



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Suppor:ng tools

§ Let’s take a look at the supporEng tools

§ If something could be done simpler and at least

with the same quality

§ Use them and forget the Eme-‐consuming

repeEve tasks and focus on the difficult ones



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Version controlling

§ How to avoid the mess dealing with the different

versions of files in projects?

§ The source code is constantly changing …

§ The AI files too …



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Version controlling Version controlling allows the development team to manage different versions of so4ware. They keep track of work and allow the team to look at the project’s past.

There are 3 kinds of version controlling systems:

§ Local

§ Distributed

§ Client-‐server



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Version controlling – local systems

§ Revision Control System

§ Source Conde Control System

§ MKS Implementer

§ History Explorer PC Rep



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Version controlling -‐ distributed

§ DCVS (Distributed CVS)

§ Monotone

§ GNU arch

§ PlasEc SCM

§ BitKeeper

§ TeamWare

PC Rep

PC Rep

PC Rep



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Version controlling – client/server

§ CVS

§ SVN

§ Codendi

§ ClearCase

§ Visual SourceSafe

§ Autodesk Valut

Client

Client

Client

Server Rep



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Suppor:ng tools & technologies

1 • Compilation

2 • Binaries packaging

3 • Testing

4 • Deployment

5 • Documenting



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Automated builds -‐ advantages § Efficiency increase

§ Development process simplificaEon

§ Increased so4ware quality

§ Reduced costs § False version removal

§ ReducEon of the development team or task relocaEon

§ Project archiving



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Automated builds The automated build tools include:

§ make

§ Maven

§ Ant § MSBuild

§ Cmake

§ and many others

Build

On-‐demand

Scheduled

Triggered



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Automated tes:ng -‐ make An example of makefile taken from QT library. PNGMAK=scripts/makefile.linux all:

cd zlib; ./configure; make cd libpng; make -f $(PNGMAK)

clean:

-cd zlib; make clean -cd libpng; make -f $(PNGMAK) clean

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Automated tes:ng -‐ Ant <project name="MyProject" default="dist" basedir="."> <description>simple example build file</description>  <property name="src" location="src"/> <property name="build" location="build"/> <property name="dist" location="dist"/> <target name="init">  <tstamp/>  <mkdir dir="${build}"/> </target> <target name="compile" depends="init” description="compile the source " >  <javac srcdir="${src}" destdir="${build}"/> </target> <target name="dist" depends="compile” description="generate the distribution" >  <mkdir dir="${dist}/lib"/>  <jar jarfile="${dist}/lib/MyProject-${DSTAMP}.jar" basedir="${build}"/> </target> <target name="clean” description="clean up" >  <delete dir="${build}"/> <delete dir="${dist}"/> </target> </project>



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Automated tes:ng -‐ Maven <project xmlns="http://maven.apache.org/POM/4.0.0" mlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.mycompany.app</groupId> <artifactId>my-app</artifactId> <packaging>jar</packaging> <version>1.0-SNAPSHOT</version> <name>Maven Quick Start Archetype</name> <url>http://maven.apache.org</url> <dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>3.8.1</version> <scope>test</scope> </dependency> </dependencies> </project>



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Automated tes:ng -‐ unit

Test A Test ABCD

vs

Test B Test C Test D

Data

Data Data Data Data

The most popular unit tesEng tool is xUnit available in many versions for all popular languages



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Automated tes:ng -‐ system

Automated GUI tes ts

perceive the applicaEon as

a black-‐box and test it from

the user’s perspecEve.

Scope of tests

Reliability of tests

Speed of testing

Machnie does not

get borred



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Code documen:ng

§ AutomaEc API documentaEon tools:

§ JavaDoc

§ DoxyGen

§ RoboDoc

§ …

Manual documentaEon

AutomaEc documentaEon

Code Doc Code Doc



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Bug tracking Issue/Bug Tracking Systems usage leads to:

§ Bener quality control § Gaining users’ feedback

§ Bener resource allocaEon § Increased users’ comfort

§ Bener informaEon flow

§ Feature request available



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Bug tracking – bug categories

CriEcal

High

Medium

Low

CosmeEc



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Bug tracking

§ Bugzilla

§ Flyspray

§ ManEs

§ Trac

§ Redmine



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Bug tracking

Usage Buga and requests

Deployment Bugs and requests

Testing Bugs

Design and implementation Ideas



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AI implementa:on



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Implementa:on

§ Transforms the design (plan) into the code

§ Problems:

§  Technology

§  Programming language



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Levels of programming languages

Logical programming

Specialised programming

General (high level) programming

Assembler programming

Machine programming

Levels of p

rogram

ming langua

ges



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1st genera:on

§ The code lines are equal to the processor instrucEons – one to one

§ Machine code



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2nd genera:on

§ Symbolic representaEon

of processor’s

instrucEons

§ Assembler language



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3rd genera:on

§ InstrucEons represent the basic operaEons

§ Oriented on programmer

§ Most popular generaEon



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4th genera:on

§ Specialised languages oriented on advanced operaEons

§ RAD tools are 4GL build over 3GL

Matlab disp('Hello, world!')

SAS data _null_; put 'Hello, world!'; run;

Postscript (Hello, world!\n) print



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5th genera:on

§ Represent not algorithms but desired outcome

§ Logical programming

§ Programming for non-‐programmers

Common Lisp (write-line "Hello, world!")

Prolog :- write('Hello, world!'),nl.



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Hybrid programming – embedding

Hybrid programming (embedding languages)

could be synergic but:

§ Increases the technological risk

§ Increases the demand on skilled developers

§ Blurs the project



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Programming paradigms

§ Paradigm is a fundamental style of computer

programming and defines the concept of

programming language

§ The most of programming languages support

various paradigms (different approaches to

problem)



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Programming paradigms

The most popular paradigms are:

§ Agent-‐oriented

§ DeclaraEve

§ Event-‐driven

§ ImperaEve

§ Metaprogramming



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Desktop applica:ons

§ C/C++

§ Java

§ C#

§ Python



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Web services – server side § Java EE

§ PHP § Python

§ Ruby § Perl

§ .NET

§ CGI (every language that could be run by server)



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Web services -‐ RIAs § Flash

§ AJAX § Java SE

§ JavaFX § FLEX

§ OpenLaszlo

§ Silverlight



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Mobile applica:ons

§ Java ME

§ C++

§ ObjecEve-‐C



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Languages popularity Posi:on Apr 2010

Posi:on Apr 2009

Programming Language

Ra:ngs Apr 2010

Delta Apr 2009 Status

1 2 C 18.058% +2.59% A

2 1 Java 18.051% -‐1.29% A

3 3 C++ 9.707% -‐1.03% A 4 4 PHP 9.662% -‐0.23% A

5 5 (Visual) Basic 6.392% -‐2.70% A

6 7 C# 4.435% +0.38% A 7 6 Python 4.205% -‐1.88% A 8 9 Perl 3.553% +0.09% A 9 11 Delphi 2.715% +0.44% A

10 8 JavaScript 2.469% -‐1.21% A

11 42 ObjecEve-‐C 2.288% +2.15% A

12 10 Ruby 2.221% -‐0.35% A 13 14 SAS 0.717% -‐0.07% A 14 12 PL/SQL 0.710% -‐0.38% A 15 -‐ Go 0.710% +0.71% A 16 15 Pascal 0.648% -‐0.07% B 17 17 ABAP 0.625% -‐0.03% B

18 20 MATLAB 0.616% +0.13% B

19 22 AcEonScript 0.545% +0.09% B

20 19 Lua 0.521% +0.03% B hnp://www.Eobe.com/index.php/content/paperinfo/tpci/index.html



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Characteris:cs -‐ domain

§ Specialised – good at something but below

average on other things

§ General purpose – no specific domain and a

compromise on all tasks



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Characteris:cs -‐ speed

§ Compiled faster

§ Interpreted slower

But:

§ Compiled start-‐up slower

§ Interpreted start-‐up faster (write-‐run-‐test cycle)

JIT compilers for interpreted languages



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Characteris:cs -‐ portability

So4ware could be portable on two levels:

§ Source code

§ Binary code

Portability does not relate only to the programming

language but also to used libraries



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Characteris:cs -‐ prototyping

Ability to verify the assumpEon:

§ Dynamic language

§ Simple syntax

§ Efficiency isn’t a main concern

§ Checks if the idea works



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Characteris:cs – target pla`orm

Target plazorm:

§ PC

§ Mobile

§ Web



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Computer languages

(C)Lisp Clips Prolog

Others

Matlab Scilab

C/C++

Java

Python

PHP

Ruby

RIA

…

Scientific perspective Programming perspective



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Why use a specialised languages

§ Popular in the research community

§ Specialized languages

§ Many libraries/toolkits and examples are

available



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Why not use them

§ Specialised not universal

§ High price

§ Simple or no GUI

§ No networking

§ Research not producEon languges



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Important programming features

Dynamic languages:

§ Do not care about types of data

§ Focus on the algorithms

§ But reject a full data control



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Prototyping:

§ Checks algorithms

§ Easily visualise data or make GUI

§ Speed is measured as the Eme of coding not

running



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ScienEfic libraries:

§ How o4en do we construct new algorithms

(Charlie Eppes Syndrome – NUMB3RS)?

§ The applicaEon is more like a large panel building

than brick building – applies to the AI problems as

well as to the „ordinary” so4



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Visual design (graphical programming): § GUI could be constructed using components § Link the components visually § Evaluate applicaEon faster

Core

GUI Core

GUI



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More important part of AI so'ware

Mechanism

Intelligent

An

Knowledge Models Rules Graphs



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Hybrid so'ware development

Research Development

Idea

Pro

duct

Replanning & redesign

Technology X

Technology X Technology Y ??

??? ??



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Programming libraries – C/C++ § MLC++ hnp://www.sgi.com/tech/mlc/

§ FANN hnp://leenissen.dk/fann/

§ Waffles hnp://waffles.sourceforge.net/

§ OpenCV ML hnp://opencv.willowgarage.com/documentaEon/cpp/ml._machine_learning.html

§ dlib C++ hnp://dlib.net/

§ VFML hnp://www.cs.washington.edu/dm/vfml/



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Programming libraries – Java

§ Java-‐ML hnp://java-‐ml.sourceforge.net/

§ Weka Engine hnp://www.cs.waikato.ac.nz/ml/

weka/

§ MLJ hnp://sourceforge.net/projects/mldev/

§ MALLET hnp://mallet.cs.umass.edu/

§ LIBSVM hnp://www.csie.ntu.edu.tw/~cjlin/libsvm/



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Programming libraries – C# § Infer.NET hnp://research.microso4.com/en-‐us/um/cambridge/projects/infernet/

§ AForge.NET hnp://code.google.com/p/aforge/

§ C# Neural Network Library hnp://franck.fleurey.free.fr/NeuralNetwork/

§ NeuronDotNet hnp://neurondotnet.freehosEa.com/

§ NeuroBox Neural Network Library hnp://www.cdrnet.net/projects/neuro/



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Programming libraries – Python

§ Orange engine hnp://www.ailab.si/orange/

§ PyML hnp://pyml.sourceforge.net/

§ MDP hnp://mdp-‐toolkit.sourceforge.net/

§ PyBrain hnp://www.pybrain.org/

§ LIBSVM hnp://www.csie.ntu.edu.tw/~cjlin/libsvm/



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Programming libraries – PHP

§ FANN PHP Extension hnp://fann.sourceforge.net/fann.html

§ Tremani Neural Network hnp://www.tremani.nl/

open-‐source/neural-‐network/

§ ANN hnp://ann.thwien.de/index.php/Main_Page



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Example I – Java

§ A simple data mining task

§ Uses Java-‐ML library



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Example II – Python

§ AdapEve filtering algorithm

§ Use as a web service (server part)

§ Use mod_python on Apache



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Example III – Flex

§ ParEcle Swarm OpEmisaEon example

§ RIA applicaEon

§ Uses a standard Flex without any addiEonal

libraries



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Thank you. Any ques:ons?

Sdart AI software developement tutorial

Technology

Transcript of Sdart AI software developement tutorial