Software Evolution and Evolutionary Computation

Bridging the Gap: The Cybernetics of Auto-Adaptive Software Systems

A.G. Laws, A. Taleb-Bendiab & S.J. Wade

This work is financed by the UK EPSRC (GR/M02958) under the Systems Engineering for Business Process Change (SEBPC) initiative.

School of Computing & Mathematical Sciences

Introduction

Biological EvolutionLamarckDarwin

Neo-darwinism

Random mutationAdaptation & Evolution

GeneticAlgorithms(Holland)

Evolutionarystudies

(Ray's Tierra)

ArtificialIntelligence

??

A-Life(Langton)

Cybernetics

ManagerialCybernetics

SoftwareEngineering

SoftwareMaintenance

SoftwareEvolution

Self-AdaptiveSoftware

Software Engineering and Maintenance The aim of Software Engineering has been described as:

“...the systematic, principled, design and deployment of applications that fulfil software’s original promise - applications that retain their full malleability throughout their lifetime and that are as easy to modify in the field as they are on the drawing board.” (Laddaga,1997)

In the thirty years since its establishment as a discipline many techniques have been introduced in pursuit of this goal, e.g. structured programming - high-level languages- OO - process maturity etc.

(Lehman, ) All require the software designer to predict every software circumstance and code to be provided to deal with the situation.

While significant improvements have undoubtedly resulted, these efforts have failed to deliver on-going malleability and most software systems exhibit a growing fragility caused by continual adaptation to meet changing requirements.

There is growing evidence to suggest that it is time to reconsider the engineering and maintenance of software systems in a similar manner to physical products and that a more biologically-oriented view should be adopted.

Software Evolution Instrumental in this changed perception has been Lehman’s many

contributions on Software Evolution: Beginning with the description of any software program as:

“...a model of a model within a theory of a model of an abstraction of some portion of the world or of some universe of discourse. (Lehman, 1980)

Allowed a software classification scheme to be derived based on the degree of homomorphism required to obtain a suitable mapping. S-type - fully specifiable i.e. a fully isomorphic mapping is obtainable. P-type - fully isomorphic mapping unobtainable, abstraction and approximation

introduced. E-type - models human activity, abstraction and assumptions embedded, changes

the problem situation, and leads to continuing change and establishes feedback loop, i.e. software operates in a changing environment

Uncertainty Principle - “...the outcome of a real world software system is uncertain and the precise area of uncertainty is also not knowable.”

Laws of Software Evolution - particularly the Eighth “Software evolution processes constitute multi-level, multi-loop, feedback systems

and must be treated as such to achieve any significant improvement over any reasonable base.” (Lehman, 1997)

Self-Adaptive Software Defined by Laddaga in the 1997 DARPA Broad Agency

Announcement as: “...software that evaluates its own performance and changes

behaviour when the evaluation indicates that it is not accomplishing what the software is intended to do...”.

To adapt, the system reacts to environmental change - the problem is recognising the need for change, then planning, enacting and verifying the change - these are management issues - self-managing systems

Progress to date has been informed by three guiding metaphors control systems theory dynamic planning systems self-aware or reflective systems.

“Managing complexity is a key goal of self-adaptive software. If a program must match the complexity of the environment in its own structure it will be very complex indeed! Somehow we need to be able to write software that is less complex than the environment in which it is operating yet operate robustly.” (Robertson, Laddaga et al, 2000)

A View of Biological Adaptation and Evolution Living systems can be described in terms of interdependent variables, each

capable of varying over a range with upper and lower bounds, e.g. bodily temperature, blood pressure, heart rate etc. Environmental change may cause fluctuations but bodily control mechanisms autonomically act to maintain variables at a stable level, i.e. homeostatic equilibrium with the environment

Three types of adaptation to environmental disturbance are available to higher organisms: Short-term change - e.g. Environmental temperature change moves the bodily

temperature variable to an unacceptable value. This rapidly induces an autonomic response in the (human) organism i.e. either perspiring to dissipate heat or shivering to generate heat. Such adaptation is quickly achieved and reversed.

Somatic change - prolonged exposure to environmental temperature change results in the impact of the change being absorbed by the organism i.e. acclimatization. Such change is slower to achieve and reverse.

Genotypic change - adaptation through mutation and hence evolution. A species adapts to change by shifting the range of some variables. e.g. in a cold climate grow thicker fur. Such genotypic change is recorded at a cellular level and becomes hereditary and is irreversible in the lifetime of the individual.

Cybernetics: The Foundations of the Bridge A cross-disciplinary approach developed in the 1940’s and

broadly encompassing contributions from biology, social sciences and nascent computer science.

Wiener defined cybernetics as

“the science of communication and control in the animal and machine”.

Ashby’s contribution... Both the system and the environment in which it exists are

represented by a set of variables that represent that form a state-determined system

Consequently, the environment is defined as those variables whose changes affect the system and those variables that are affected by the system.

Complexity as Variety, i.e. The number of different states a system can adopt.

Ashbean Cybernetics The Homeostat - ultra-stable system

capable of returning to stability after it has been disturbed in a way not envisaged by the designer.

Self-vetoing homeostasis Variety Engineering

The notion of balancing the varieties of systems with different variety levels

Environment - huge variety Operation - much less variety Management - even less variety

Achieved through attenuation and amplification

The Law of Requisite Variety control can only be attained if the variety of the controller is at least as great as the situation to be controlled.

K eyV = V a rie ty

E n v iro n m en t

V V

S y stem

M a n a g e m e n tU n it

V

= A tten u a tion

= A m p lifica tion

Managerial Cybernetics Beer’s VSM implements a control

& communication structure via hierarchies of homeostats (feedback loops)

6 major systems ensure ‘viability’ of the system Implementation S1 Monitoring S2 Audit S3* Control S3 Intelligence S4 Policy S5

Offers an extensible, recursive, model-based architecture, devolving autonomy to sub-systems

Here andNow

Future

Homeostasis

N am e of th e V iab le S ys tem in F ocu s :

T h e V ia b le S ystem M od e l

F orm at © S . B ee r 1 9 8 5

F IV E - P olicy

Sys tem F OU RE x te rn a l & F u tu reS e lf-re fe re n c eS im u la tio nP la n n in g

Sys tem THR E EIn te rn a l & C u rre n tS e lf-o rg a n iz a tio nA u to n o m ic re g u la tio n

ON E

ON E

ON E

L o ca lR eg u la to ry

L o ca lR eg u la to ry

L o ca lR eg u la to ry

S y s te m T W Oa n ti-o sc illa to ry

S y s te m T H R E E *S p o ra d ic A u d it

T W O L o ca la n ti-o sc illa to ry

T W O L o ca la n ti-o sc illa to ry

T W O L o ca la n ti-o sc illa to ry

F u tu re

T O T A LE n v iro n m en t

Direction

What We’re Doing With This Our first attempts saw the

design of a Viable Adaptive Agent Architecture.

Based on Bratman et al’s IRMA approach, the design incorporates a beliefs, desires, intentions (BDI) approach.

The design specifies, in some detail, the higher, deliberative processes of adaptation.

In some respects, the ready-mapping shows the applicability of the VSM-based approach.

Desires

Wor

ldM

odel Deliberation

Process

Intentions

OpportunityAnalyser

EnvironmentalScan

FilteringProcess

Plans

S

urvi

ving

Pla

ns

Intentions

Planning Process

PlanLibrary

Reasoner

Internal Model

InternalModel

Status

ResourceBargaining

Process

Intentions

Overall Plans

Resources

Acco

unta

bilit

y Intentions

System Three

System Four

System Five

Operational

Status

Scheduling

Ov er a ll Sch e dul eLocal Plans

Local Schedule

Local Monitoring data

Local Monitoring data

Local Monitoring data

Ove

rall

Sche

dule

Mon

itorin

g to

nex

t rec

ursio

n

Monitoring data

Perhaps more excitingly: A Practical ApplicationIn-Vehicle Telematics System (IVTS)

IVTS cover a broad range of computer-based systems, these may include or combine: Engine Management Systems Driver’s Information Systems Vehicle Navigation & Tracking Systems Collision Avoidance Systems Vehicle Security Systems

Using Jini + Java RMI allows distributed applications to be developed as a series of clients that interact with application services via a look-up service.

The service-oriented abstraction considers a service as a logical concept that can be discovered dynamically by a client and used according to a contract of use

S ys te m 5P o lic y

S ys te m 4In te llig e n c e

S ys te m 3C o n t ro l

System 3*A udit

V eh icle 1 (S1)

5

4

3

2

2

2

2

3 *

1

Org

an

iza

tion

al E

nvi

ron

me

nt

S y stem 2C o o rd in a tio n

S y stem 2C o o rd in a tio n

V eh icle 2 (S1)

5

3

43 *

1

S y stem 2C o o rd in a tio n

V eh icle 3 (S1)

5

3

43 *

1

S y stem 2C o o rd in a tio n

More IVTS

Removable In-VehicleComputer / Mobile Data

Terminal

EngineManagement

Anti-SlipBraking

High Speed Serial Bus (CAN 2.0 / SAE J1939)

Low Speed Serial Bus (SAE J1850)

DriverInformation

System

TrafficInformation

(TrafficMaster)

USB

/ IEEE

1394

PC

MIA

DocumentScanner

Printer

Wireless WAN(GSM)

Wireless LAN(Spread Spectrum /

Bluetooth)

CD ROM

DockingCradle

RadarCollision

Avoidance

AutonomousDriving

RouteGuidance(CARIN)

AccidentRecording

INTEGRATED IN-VEHICLE

TELEMATICS SYSTEM

More IVTS

More IVTS

Monitoring & Instrumentation Considerations In order to debug, monitor and

manage Jini applications, a dynamic instrumentation system, based again on Jini technology has been developed

Uses: Monitor service - connects with an

application or lookup service and observes dynamic parameters

Logger utility - invoked by a monitor to record data

Analyzer utility - invoked by a monitor to compute complex parameters

A Management Service creates and controls the monitor service

LookupServices

ApplicationServices

JVMsMonitorServices

ManagementServices

AnalyzerUtilities

ObservationsandMeasurements

MonitorConfiguration

Jini ApplicationInstrumentationSystem

LoggerUtilities

Clients

The instrumentation system is implemented by combining Jini’s code mobility capabilities Java’s dynamic proxies Jini’s event models

To apply instrumentation: the management service requests

the application service to set an instrumented parameter

The Look-up service monitor detects a change of state and notifies client

Management service wraps service in monitor service and sends proxy to client

Some Implications and Final Comments

The complete realization of a self-adaptive software system using the conceptual guidance of classical cybernetics and the VSM still has some way to go. Indeed, it isn’t yet clear that full realization is possible and there are still many problems outstanding.

Nevertheless, using off-the-shelf technology that does not necessarily reflect this viewpoint exactly has allowed us to make significant progress and indicates that this approach may supply a ready-made theoretical underpinning for self-adaptive software.

Such approaches herald an exciting new era in software development and offer the potential to realize the fulfil software’s original promise .