Requirements vs. Design vs. Runtime

UC San DiegoCSE 294

Fall Quarter 2007Barry Demchak

2

Papers

A Reconfiguration Mechanism for Statechart Based Components by Elkorobarrutia, Sagardui, et al (2007)

Reconciling System Requirements and Runtime Behavior by Feather, Fickas, et al (1998)

3

The Questions

Can requirements and designs be leveraged to facilitate system adaption … yet avoid emergent, inappropriate, or unpredictable behavior?

What types of requirements and designs facilitate this?

What types of runtime support facilitate this?

4

The Context – Autonomic Computing

Autonomic Computing as defined by Kephart and Chess in IEEE Computer Society, January 2003

Systems and integration of systems is getting more complex (installation, tuning, configuration, maintainance)

Design getting too complex push some design decisions to runtime

self-* Self-configuration – based on high level policies Self-optimization Self-healing – detect, diagnose, repair Self-protection – malicious attacks, cascading failures

5

Two Approaches

Statechart execution (Elkorobarrutia et al)

Goal-oriented (Feather et al)

6

Statechart Execution Basic Approach

Given a collection of substitutable components, have an application-independent way of choosing one No designer involvement needed Operates at higher level (i.e., component) than

Class/Operation/Type (as in Java methodology per Faud)

Develop statechart-based components Assist developer in creating executable components Make model available at runtime (i.e., reflection) Based on message dispatcher/receptor model (i.e.,

perhaps a leadin to SOA)

7

Sample Statechart

r o o t=new XorSt a t e ( ) ;r o o t S t a t e=r o o t ;s1=new XorSt a t e ( ) ;r o o t . addSt a t e ( s1 ) ;s2=new AndState ( ) ;r o o t . a d d I n i t i a l S t a t e ( s2 ) ;r eg1=new Region ( ) ;s2 . addRegion ( r eg1 ) ;s21=new XorSt a t e ( ) ; ...

Structure:

j r=new J o i nRe a c t i o n ( s1 , n u l l , ” e vA j o i n ” ) ;

j r . addSource ( s22 ) ;

j r . addSource ( s24 ) ;

s2 . addRe a c t ion (EvA . c l a s s , j r ) ;

s1 . s e t E x i tAc t i o n ( ” e x i t 1 ” ) ;

. . . . . . .

Behavior:

8

State Machine Execution

1. Incoming Message

2. Evaluate Guards 3. Execute action/transition

State Machine

Repository

2. Replace

state

machine,

add states,

remove

states

1. Incoming Message

9

Executor Model

Well Tested Interceptor site Exception catching Replace executor

Can throw exception

10

Advantages

Direct connection to UML model – requirements are inferred

Execution chosen based on user stimulus or automatic reaction to execution fault

Inversion of Control (IoC) allows flexible choice of execution alternatives

11

Disadvantages

Undefined semantics for changed statecharts No guarantees that statecharts are correct Independent execution framework was not

achieved (interceptors and exception handlers oddly conceived)

12

Goal-oriented Basic Approach

Identify high level goals and the services, constraints, and agents that serve them … then figure out way to achieve the goal

Allow multiple execution strategies, and provide the means to choose between them

Monitor system behavior at runtime, and reconcile it with requirements (-- complete static analysis too costly, and may not even be possible)

13

Closing the Gap (requirements vs runtime)

At specification time, identify event sequences to be monitored – acceptable vs unacceptable changes

At design time, identify alternative designs and explicitly represent them in trees

At runtime, monitor system state to determine when goals are not met, then take remedial action – change parameters or choose alternate design

14

The Players

Object – entities, relationships, events characterized by attributes and invariants

Actions – input/output relation over objects Agent – object that acts as action processor Goal – contains AND-refinement of subgoals and

OR-refinement of alternative subgoals Constraint – ultimate decomposition of goals Assumption – auxiliary assertions

KAOS – Specification Language FLEA – Monitoring System

15

KAOS – Specification Language

16

Temperal Logic Operators Cheat Sheet

o (in the next state)

· (in the previous state)

(eventually)

(some time in the past)

(always in the future)

(always in the past)

U (always in the future until)

W (always in the future unless)

Subscripts as realtime restrictions: nu means “sometime in the future within n time units u”.

17

KAOS – Specification Language

Effectively, goal requirements are in constraints

18

FLEA – Runtime Monitor

FLEA Language provides constructs for expressing temporal combinations of events

Runtime code to monitor such events is automatically generated by FLEA.

Consists of: Historical database of events Inference engine Communication system

Gathers events Distributes notifications of event combinations

19

FLEA Temporal Patterns

20

More FLEA

Events can be composed using boolean operators

External events are allowed Definition: (defevent constraints-provided: external (string))

Instance: (constraints-provided 1365124 chp)

Events are used for: Detecting violations of complex assertions Keeping track of frequency of violations Deciding when it’s time to react

21

System Architecture

22

KAOS Spec Refinement Graph

23

Requirements-Runtime ReconciliationDevelopment Level

Goal-based specification is elaborated KAOS assertions that can be violated are

identified – breakable assertions KAOS specification implemented as system

of cooperating agents that generate traces of breakable assertions

24

Requirements-Runtime ReconciliationDevelopment Level

D1 Elaborate the goal refinement graph, identify breakable assertions, and formalize into temporal pattern

D2 Check for monitorability, identify monitored parameters

D3 Identify reconciliation tactics (i.e., change a control parameter or switch to alternative design)

D4 Translate breakable assertions into FLEAD5 Bind trace events to particular agent modules

25

Requirements-Runtime ReconciliationRuntime Level

R1 Establish communication between state information and runtime monitor

R2 Correlate FLEA event definitions with current design specification

R3 Start monitoring events and looking for broken assertions

26

KAOS-FLEA Recap

Preserves high level goals through Requirements-time reasoning Event-based runtime monitoring System self-adaption tactics

Advantages Development artifacts intimately tied to runtime

decisions End-to-end demonstration of efficient execution

Disadvantages Difficult ADL Awkward integration between KAOS and FLEA layers

27

Methodology Comparison

Both Elevate runtime problem resolution to

requirements/design level Employ IoC methods in choosing amongst alternatives Self-healing, but in different ways

Statecharts Tied to UML Untried end-to-end, many basic questions remain

KAOS-FLEA Tied to custom ADL and temporal logic Solid end-to-end demonstration

28

The Questions (again)

Can requirements and designs be leveraged to facilitate system adaption … yet avoid emergent, inappropriate, or unpredictable behavior?

What types of requirements and designs facilitate this?

What types of runtime support facilitate this?

29

Bibliography1. M. Feather, S. Fickas, A. van Lamsweerde, and C. Ponsard, Reconciling System

Requirements and Runtime Behavior, Proc. IWSSD'98 - 9th International Workshop on Software Specification and Design, Isobe, IEEE CS Press, April 1998.

2. M. Rohr, M. Boskovic, S. Gieseck, and W. Hasselbring. Model-driven Development of Self-managing Software Systems. Proceedings of the Workshop ``Models@run.time'' at the 9th International Conference on Model Driven Engineering Languages and Systems (MoDELS/UML'06) 2006. Springer, 4.

3. X. Elkorobarrutia, G. Sagardui, and X Aretxandieta. A Reconfiguration Mechanism for Statechart Based Components. Proceedings of the 1st Workshop on Model-driven Software Adaptation (M-ADAPT’07 at ECOOP 2007). Berlin, Germany, 2007.

4. F. Barbier. MDE-based Design and Implementation of Autonomic Software Components. Proceedings of the 5th IEEE International Conference on Cognitive Informatics (ICCI’06). Beijing, China. July, 2006.

5. J. Kephart and D. Chess. The Vision of Autonomic Computing. IEEE Computer 36(2003).6. G. Blair, N. Bencomo. Workshop Models@run.time.

http://www.comp.lancs.ac.uk/~bencomo/MRT07. June, 2007.7. M. Faud, D. Deb, M. Oudshoorn. Adding Self-Healing Capabilities into Legacy Object

Oriented Applications. Proceedings of the International Conference on Autonomic and Autonomous Systems (ICAS). P 15. July, 2006.