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Workshop on Configuration Vienna, Aug. 29 th -30 th , 2013 Towards - - PowerPoint PPT Presentation

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Institute for Software Technology Workshop on Configuration Vienna, Aug. 29 th -30 th , 2013 Towards Anomaly Explanation in Feature Models Alexander Felfernig 1 , David Benavides 2 , Jos Galindo 2 , and Florian Reinfrank 1 1 Graz University of

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SLIDE 1

Alexander Felfernig

Institute for Software Technology 1

Towards Anomaly Explanation in Feature Models

Alexander Felfernig1, David Benavides2, José Galindo2, and Florian Reinfrank1

1Graz University of Technology, Austria 2University of Seville, Spain

Workshop on Configuration

Vienna, Aug. 29th-30th, 2013

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SLIDE 2

Alexander Felfernig

Institute for Software Technology 2

Overview

  • Introduction
  • 1. Feature Models (FMs): Modeling Concepts
  • 2. FMs: Configuration Task Definition
  • 3. FMs: Analysis Operations
  • Testing & Debugging
  • 4. Configuration Models: Testing & Debugging
  • 5. FM Analysis Operations as Test Cases
  • 6. FM Analysis Operations & Explanations
  • Ongoing & Future Work

Feature Modeling Configuration

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Alexander Felfernig

Institute for Software Technology 3

Feature Models (FMs): Modeling Concepts

„XOR“ „OR“

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Alexander Felfernig

Institute for Software Technology 4

FMs: Configuration Task Definition

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Alexander Felfernig

Institute for Software Technology 5

Configuration Task: Example

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Alexander Felfernig

Institute for Software Technology 6

FMs: Analysis Operations

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SLIDE 7

Alexander Felfernig

Institute for Software Technology 7

Configuration Models: Testing & Debugging

c1 c2 c3 c4 c5 ck

Constraints (CF)

t1 t2 t3 tn

Test Cases (T)

c1 c4 c1 c5 c3 c4 c3 c5

Diagnosis ()

tj  T: inconsistent(CF  tj)

Explanation   CF: consistent (CF -   tj) tj  T

  • A. Felfernig, G. Friedrich, D. Jannach, and M. Stumptner,

Consistency-based Diagnosis of configuration knowledge bases, in Artificial Intelligence, 152(2), 2004, pp. 213–234.

Conflicts

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Alexander Felfernig

Institute for Software Technology 8

FM Analysis Operations as Test Cases

Example analysis operation: „Dead feature“ fi  F ? inconsistent (CF  {fi = true}  {c0}) Test Case: tj  T tj: fi = true Explanation   CF: consistent (CF -   {fi = true})

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Alexander Felfernig

Institute for Software Technology 9

FM Analysis Operations & Explanations

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Alexander Felfernig

Institute for Software Technology 10

Explanations: Used Algorithms

  • Preferred conflicts (minimal)
  • HSDAG with test cases
  • Preferred diagnoses (minimal): FastDiag
  • Redundant constraints: FMCore
  • U. Junker. QuickXplain: Preferred explanations and relaxations for over-constrained problems.

AAAI’04, pp. 167–172, 2004.

  • A. Felfernig, M. Schubert, and C. Zehentner. An efficient diagnosis algorithm for inconsistent

constraint sets. AIEDAM, 26(1):53–62, 2012. Alexander Felfernig , D. Benavides, J. Galindo, F. Reinfrank. Towards Anomaly Explanation in Feature Models, Workshop on Configuration, pp. 117-124, Vienna, Austria, 2013.

  • A. Felfernig, G. Friedrich, D. Jannach, and M. Stumptner, Consistency-based Diagnosis of

configuration knowledge bases, in Artificial Intelligence, 152(2), 2004, pp. 213–234.

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SLIDE 11

Alexander Felfernig

Institute for Software Technology 11

Evaluation

  • A. Felfernig, M. Schubert, and C. Zehentner.

An efficient diagnosis algorithm for inconsistent constraint sets. AIEDAM, 26(1):53–62, 2012.

  • R. Reiter. A theory of

diagnosis from first principles. Artificial Intelligence, 32(1):57–95, 1987.

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Alexander Felfernig

Institute for Software Technology 12

Ongoing & Future Work

  • Further evaluation of algorithms (ongoing work with

University of Seville)

  • Additional analysis operations (e.g., taking into

account multiplicity bounds)

  • Improved prediction of the sources of faulty behavior

(e.g., exploitation of eye tracking „confusion patterns“)

  • Algorithms for intra-constraint redundancies
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Alexander Felfernig

Institute for Software Technology 13

Conclusions

  • Approach to integrate contributions of “Feature

Modeling” and “Configuration” communities

  • Diagnosis & redundancy detection as a basis for the

explanation of “well-formedness” violations

  • Generation of test cases on the basis of feature

model analysis operations

  • No additional management overheads for the

generated test cases

  • Not a substitute for “conventional” KB testing!
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SLIDE 14

Alexander Felfernig

Institute for Software Technology 14

Thank You!