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Aditya P. Mathur

Professor, Department of Computer Science,

Associate Dean, Graduate Education and International

Programs

Purdue University

Department of Computer ScienceNorth Dakota State University, Fargo, NDThursday April 19, 2007

Saturation effect and the need for a new theory of software reliability

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Dependability

Availability: Readiness for correct service

Reliability: Continuity of correct service

Safety: Absence of catastrophic consequences on the user(s) and the

environment

Security: The concurrent existence of (a) availability for authorized

users only, (b) confidentiality, and (c) integrity.

Source: Wikipedia.

Focus of this talk

The presence of software errors has the potential for negative impact on each aspect of Dependability.

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Reliability

Probability of failure free operation in a given environment

over a given time.

Mean Time To Failure (MTTF)

Mean Time To Disruption (MTTD)

Mean Time To Restore (MTTR)

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Operational profile

Probability distribution of usage of features and/or scenarios.

Captures the usage pattern with respect to a class of customers.

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Reliability estimation-Early Work

Operationalprofile

Random or semi-random Test generation

Test execution Failure data collection

Reliability estimation

Decision process

[Shooman ‘72, Littlewood ‘73, Musa ‘75, Thayer ‘76, Goel et al. ‘78, Yamada et al. ‘83, Laprie ‘84, Malaiya et al. ‘92, Miller et al. ‘92, Singpurwalla ‘95]

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Reliability estimation: Correlation, Coverage, Architecture

Cheung ’80: Markovian model

Ohba ’92. Piwowarski et al.‘93: Coverage based

Chen et al. ’92: Coverage based

Garg ’95, Del Frate et al.’95 : Coverage/reliability model and correlation.

Littlewood ’79: architecture based

Malaiya et al. ’94: Coverage based

Xiaoguang et al. ‘03: architecture based

Krishnamurthy et al. ’97: architecture based

Gokhale et al. ’98: architecture based

Chen et al. ’94, Musa ’94: Reliability/testing sensitivity

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Need for Ultrahigh Reliability

Medical devices

Automobile engine controllers

Aircraft controllers

Track/train control systems

No known escaped defects that might create unsafe situations and /or might lead to ineffective performance.

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A reliability estimation scenario (slightly unrealistic)

An integrated version of the software P for a cardiac pacemaker is available for system test.

Operational profile from an earlier version of the pacemaker is available.

P has never been used in any implanted pacemaker.

Tests are generated using the operational file and P tested.

Three distinct failures are foundand analyzed.

The management asks the development team to debug P and remove causes of all failures.

The updated P is retested using the same operational profile. No failures are observed. What is the reliability of the updated P?

Unrealistic

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Issues: Operational profile

Variable. Becomes known only after customers have access to the product. Is a stochastic process…a moving target!

Random test generation requires an oracle. Hence is generally limited to specific outcomes, e.g. crash, hang.

In some cases, however, random variation of input scenarios is useful and is done for embedded systems.

Human heart: Variability across humans and over time.

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Issues: Failure data

Should we analyze the failures?

If yes then after the cause is removed, the reliability estimate is invalid.

If the cause is not removed, because the failure is a “minor incident,” then the reliability estimate corresponds to irrelevant incidents.

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Issues: Model selection

Rarely does a model fit the failure data.

Model selection becomes a problem. ~200 models to choose from? New ones keep arriving!

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Issues: Markovian models

Markov models suffer from a lack of estimate of transition probabilities.

To compute these probabilities, you need to execute the application.

During execution you obtain failure data. Then why proceed further with the model?

C1

C3

C212

13 32

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12 + 13=1

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Issues: Assumptions

Software does not degrade over time; e.g. memory leak is not degradation and is not a random process; a new version is a different piece of software.

Reliability estimate varies with operational profile. Different customers see different reliability.

Can we have a reliability estimate independent of the operational profile?

Can we not advertise quality based on metric that are a true representation of reliability..not with respect to a subset of features but over the entire set of features?

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Sensitivity of Reliability to test adequacy

Coverage

low

low

high

high

Desirable

Suspect modelUndesirable

Risky

Rel

iabi

lity

Problem with existing approaches to reliability estimation.

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Basis for an alternate approach

Why not develop a theory based on coverage of testable items and test adequacy?

Testable items: Variables, statements,conditions, loops, data flows, methods, classes, etc.

Pros: Errors hide in testable items.

Cons: Coverage of testable items is inadequate. Is it a good predictor of reliability?

Yes, but only when used carefully. Let us see what happens when coverage is not used or not used carefully.

Are we interested in reliability or in confidence?

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Saturation Effect

FUNCTIONAL, DECISION, DATAFLOWAND MUTATION TESTING PROVIDETEST ADEQUACY CRITERIA.

Reliability

Testing EffortTrue reliability (R)Estimated reliability (R’)Saturation region

Mutation

Dataflow

Decision

Functional

RmRdfRdRf

R’f R’d R’df R’m

tfs tfe tds tde tdfs tdfe tms tfe

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An experiment

Tests generated randomly exercise less code than those generated using a mix of black box and white box techniques. Application: TeX. Creator: Donald Knuth. [Leath ‘92]

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Modeling an application

OSComponent

Component

Component

Interactions

Component

Component

Component

Interactions

Component

Component

Component

Interactions

……….

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Reliability of a component

R(f)= (covered/total), 0<<1.

Reliability, probability of correct operation, of function f based on a given finite set of testable items.

Issue: How to compute ?

Approach: Empirical studies provide estimate of and its variance for different sets of testable items.

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Reliability of a subsystem

R(C)= g(R(f1), R(f2), ..R(fn), R(I))

C={f1, f2,..fn} is a collection of components that collaborate with each other to provide services.

Issue 1: How to compute R(I), reliability of component interactions?

Issue 2: What is g ?

Issue 3: Theory of systems reliability creates problems when (a) components are in a loop and (b) are dependent on each other.

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Scalability

Is the component based approach scalable?

Powerful coverage measures lead to better reliability estimates whereas measurement of coverage becomes increasingly difficult as more powerful criteria are used.

Solution: Use component based, incremental, approach. Estimate reliability bottom-up. No need to measure coverage of components whose reliability is known.

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Next steps

Develop component based theory of reliability.

[Littlewood 79, Kubat 89, Krishnamurthy et al. 95, Hamlet et al. 01, Goseva-Popstojanova et al. 01, May 02]

Do experimentation with large systems to investigate the applicability and effectiveness in predicting and estimating various reliability (confidence) metrics.

Base the new theory on existing work in software testing and reliability.

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The Future

Apple Confidence: 0.999

Level 0: 1.0

Level 1: 0.9999

Level 2: 0.98

Boxed and embedded software with independently variableLevels of Confidence.

Mackie Confidence: 0.99

Level 0: 1.0

Level 1: 0.9999