An Experimental Evaluation on Reliability Features of N-Version Programming

Xia Cai, Michael R. Lyu and Mladen A. Vouk

ISSRE’2005

2

Outline

Introduction

Motivation

Experimental evaluation

• Fault analysis

• Failure probability

• Fault density

• Reliability improvement

Discussions

Conclusion and future work

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Introduction

N-version programming is one of the main techniques for software fault tolerance

It has been adopted in some mission-critical applications

Yet, its effectiveness is still an open question

• What is reliability enhancement?

• Is the fault correlation between multiple versions a big issue that affects the final reliability?

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Introduction (cont’)

Empirical and theoretical investigations have been conducted based on experiments, modeling, and evaluations• Avizienis and Chen (1977), Knight and Leveson (1986), Kelly and

Avizienis (1983), Avizienis, Lyu and Schuetz (1988), Eckhardt et al (1991), Lyu and He (1993)

• Eckhardt and Lee (1985), Littlewood and Miller (1989), Popov et al. (2003)

• Belli and Jedrzejowicz (1990), Littlewood. et al (2001), Teng and Pham (2002)

No conclusive estimation can be made because of the size, population, complexity and comparability of these experiments

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Research questions

What is the reliability improvement of NVP?

Is fault correlation a big issue that will affect the final reliability?

What kind of empirical data can be comparable with previous investigations?

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Motivation

To address the reliability and fault correlation issues in NVP

To conduct a comparable experiment with previous empirical studies

To investigate the “variant” and “invariant” features in NVP

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Experimental background

Some features about the experiment• Complexity

• Large population

• Well-defined

• Statistical failure and fault records

Previous empirical studies• UCLA Six-Language project

• NASA 4-University project

• Knight and Leveson’s experiment

• Lyu-He study

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Experimental setup

RSDIMU avionics application

34 program versions

A team of 4 students

Comprehensive testing exercised• Acceptance testing: 800 functional test cases and 400 random

test cases

• Operational testing: 100,000 random test cases

Failures and faults collected and studied

Qualitative as well as quantitative comparisons with NASA 4-University project performed

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Experimental description

Geometry Data flow diagram

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Comparisons between the two projects

Qualitative comparisons

• General features

• Fault analysis in development phase & operational test

Quantitative comparisons

• Failure probability

• Fault density

• Reliability improvement

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General features comparison

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Faults in development phase

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Distribution of related faults

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Fault analysis in development phase Common related faults

Display module (easiest part)

Calculation in wrong frame of reference

Initialization problems

Missing certain scaling computation

Faults in NASA project only Division by zero

Incorrect conversion factor

wrong coordinate system problem.

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Fault analysis in development phase (cont’)

Both cause and effect of some related faults remain the same

Related faults occurred in both easy and difficult subdomains

Some common problems, e.g., initialization problem, exist for different programming languages

The most fault-prone module is the easiest part of the application

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Faults in operational test

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Faults in operational test (cont’)

These faults are all related to the same module, i.e., sensor failure detection and isolation problem

Fault pair (34.2 & 22.1) : 25 coincidence failures

Fault pair (34.3 & 29.1) : 32 coincidence failures

Yet these two pairs are quite different in nature

Version 34 shows the lowest quality • Poor program logic and design organization

• Hard coding

The overall performance of NVP derived from our data would be better if the data from version 34 are ignored

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Input/Output domain classification

Normal operations are classified as:

Si,j = {i sensors previously failed and

j of the remaining sensors fail

| i = 0, 1, 2; j = 0, 1 }

Exceptional operations: Sothers

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Failures in operational test

States S0,0, S1,0 and S2,0 are more reliable than states S0,1, S1,1, S2,1

Exceptional state reveals most of the failures

The failure probability in S0,1 is the highest

The programs inherit high reliability on average

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Coincident failures

Two or more versions fail at the same test case, whether the outputs identical or not

The percentage of coincident failures versus total failures is low:• Version 22: 25/618=4%

• Version 29: 32/2760=1.2%

• Version 32: (25+32)/1351=4.2%

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Fault density

Six faults identified in 4 out of 34 versions

The size of these versions varies from 1455 to 4512 source lines of code

Average fault density:

• one fault per 10,000 lines

It is close to industry-standard for high quality software systems

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Failure bounds for 2-version system

Lower and upper bounds for coincident failure probability under Popov et al model

DP1: normal test cases without sensor failures dominates all the testing cases DP3: the test cases evenly distributed in all subdomains DP2: between DP1 & DP3

Version pair

DP1 DP2 DP3

Lower bound

Upper bound

Lower bound

Upper bound

Lower bound

Upper bound

(22,34) 0.000007 0.000130 0.000342 0.006721 0.000353 0.008396

(29,34) 0.000000 0.000001 0.000009 0.000131 0.000047 0.000654

Average in our project

1.25*10-8 2.34*10-7 6.26*10-7 0.000012 7.13*10-7 0.000016

Average in NASA project

2.32*10-7 0.000007 0.000023 0.000103 0.000072 0.000276

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Quantitative comparison in operational test

NASA 4-university project: 7 out of 20 versions passed the operational testing

Coincident failures were found among 2 to 8 versions

5 out of 7 faults were not observed in our project

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Observations

The different on fault number and fault density is not significant

In NASA project:

• The number of failures and coincident failures in NASA project is much higher

• Although there is coincident failures in 2- to 8-version combinations, the reliability improvement for 3-version system still achieves 80~330 times better

In our project:

• Average failure rate is 50 times better

• The reliability improvement for 3-version system is 30~60 times better

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Invariants

Reliable program versions with low failure probability

Similar number of faults and fault density

Distinguishable reliability improvement for NVP, with 102 to 104 times enhancement

Related faults observed in both difficult and easy parts of the application

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Variants

Compared with NASA project, our project:

• Some faults not observed

• Less failures

• less coincident failures

• Only 2-version coincident failures (other than 2- to 8- version failures)

• The overall reliability improvement is an order of magnitude larger

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Discussions

The improvement of our project may attributed to

• stable specification

• better programming training

• experience in NVP experiment

• cleaner development protocol

• different programming languages & platforms

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Discussions (cont’)

The hard-to-detected faults are only hit by some rare input domains

New testing strategy is needed to detect such faults:

• Code coverage?

• Domain analysis?

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Conclusion

An empirical investigation is performed to evaluate reliability features by a comprehensive comparisons on two NVP projects

NVP can provides distinguishable improvement for final reliability according to our empirical study

Small number of coincident failures provides a supportive evidence for NVP

Possible attributes that may affect the reliability improvement are discussed

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Future Work

Apply more intensive testing on both Pascal and C programs

Conduct cross-comparison on these program versions developed by different programming languages

Investigate the reliability enhancement of NVP based on the combined set of program versions

Thank you !

Q & A