Wissensverarbeitung. - Knowledge Engineering: Feature Models -

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1 - Knowledge Engineering: Feature Models - Alexander Felfernig und Gerald Steinbauer Institut für Softwaretechnologie Inffeldgasse 16b/2 A-8010 Graz Austria 1

2 Goals Feature Models Semantics Anomalies Inconsistencies Redundancies Violation of Well-Formedness Exercises 2

3 Semantics of Feature Models [Benavides et al. 2009] 3

4 Mandatory Relationships: Semantics A B MobilePhone Calls MobilePhone Screen 4

5 Optional Relationships: Semantics B A GPS MobilePhone Media MobilePhone 5

6 OR Relationships: Semantics A (B1 B2 Bn) Media (Camera MP3) 6

7 Alternative Relationships: Semantics (B1 ( B2 Bn A )) (Bn ( B1 Bn-1 A )) (Basic ( Colour HighRes Screen)) (Colour ( Basic HighRes Screen)) (HighRes ( Colour Basic Screen)) 7

8 Requires Relationships: Semantics A B Camera HighRes 8

9 Excludes Relationships: Semantics (A B) (GPS Basic) 9

10 Integrated Knowledge Base c 1 : MobilePhone Calls c 2 : MobilePhone Screen c 3 : GPS MobilePhone c 4 : Media MobilePhone c 5 : Media (Camera MP3) c 6 : (Basic ( Colour HighRes Screen)) (Colour ( Basic HighRes Screen)) (HighRes ( Colour Basic Screen)) c 7 : Camera HighRes c 8 : (GPS Basic) 10

11 Next Example: Translation =? 11

12 Mandatory Fahrrad Typ Fahrrad Reifen 12

13 Optional Optionen Fahrrad 13

14 OR Optionen (Pumpe Gepäckträger) 14

15 Alternative (RacerBike ( CityBike MountainBike Typ)) (CityBike ( RacerBike MountainBike Typ)) (MountainBike ( RacerBike CityBike Typ)) (Track ( Racer Reifen)) (Racer ( Track Reifen)) 15

16 Requires RacerBike Racer 16

17 Excludes (CityBike Racer) (MountainBike Gepäckträger) 17

18 Integrated Knowledge Base c 1 : Fahrrad Typ c 2 : Fahrrad Reifen c 3 : Optionen Fahrrad c 4 : Optionen (Pumpe Gepäckträger) c 5 : (RacerBike ( CityBike MountainBike Typ)) (CityBike ( RacerBike MountainBike Typ)) (MountainBike ( RacerBike CityBike Typ)) c 6 : (Track ( Racer Reifen)) (Racer ( Track Reifen)) c 7 : RacerBike Racer c 8 : (CityBike Racer) c 9 : (MountainBike Gepäckträger) 18

19 Anomalies in Feature Models Anomalies are patterns that do not conform to a well defined notion of normal behavior [Chandola et al. 2009] Negative Impacts: -- Maintainability -- Readability -- Stability Types of Anomalies Inconsistencies Redundancies Unsatisfied Well-Formedness Properties 19

20 Example: Inconsistencies c 0 : Fahrrad c 1 : Fahrrad Typ Fahrrad c 1 c 2 c 2 : Fahrrad Reifen Typ c 3 Reifen c 3 : (Typ Reifen) Inconsistency: solution for {c 0, c 1, c 2, c 3 } 20

21 Example: Resolving Inconsistencies c 0 : Fahrrad c 1 : Fahrrad Typ Fahrrad c 1 c 2 c 2 : Fahrrad Reifen Typ c 3 Reifen c 3 : (Typ Reifen) Minimal Diagnosis (D): D {c 1,c 2,c 3 }: consistent({c 0,c 1,c 2,c 3 } D) and D : D D. Minimal diagnoses: {c 3 },? 21

22 Example: Redundancies c 0 : Fahrrad c 1 : Fahrrad Typ c 2 : Fahrrad Reifen Typ Fahrrad c 1 c 2 c 3 Reifen c 3 : Typ Reifen Redundancy: {solutions for {c 0,c 1,c 2,c 3 }} = {solutions for {c 0,c 1,c 2 }} redundant(c i {c 0,c 1,c 2,c 3 }): {c 0,c 1,c 2,c 3 }-{c i } = {c i } 22

23 Example: Resolving Redundancies c 0 : Fahrrad c 1 : Fahrrad Typ c 2 : Fahrrad Reifen Typ Fahrrad c 1 c 2 c 3 Reifen c 3 : Typ Reifen Minimal Core (C): C {c 1,c 2,c 3 }: C {c 0 } { c 0 c 1 c 2 c 3 } inconsistent and C : C C. {c 0,c 1,c 2,c 3 } {c 3 } = max. set of non-redundant constraints. 23

24 Example: Ensuring Well-Formedness c 0 : Fahrrad c 1 : Typ Fahrrad c 2 : Fahrrad Reifen Typ Fahrrad c 1 c 2 c 3 Reifen c 3 : (Typ Reifen) Minimal Diagnosis (D): D {c 1,c 2,c 3 }: V {Fahrrad, Typ, Reifen}: consistent({c 0, c 1, c 2, c 3, c V : V} D) and D : D D. 24

25 Exercises a. Design a feature model (and the CSP) groups of 3-4 students domain not discussed in course b. Insert anomalies to your feature model Inconsistencies Redundancies Violations of well-formedness rules 25

26 References D. Benavides, S. Segura, A. Cortés: Automated analysis of feature models 20 years later: A literature review. Inf. Syst. 35(6): , V. Chandola, A. Banerjee, and V. Kumar: Anomaly Detection: A Survey, ACM Computing Surveys, 41(3):15, N. E en and N. Sörensson: Translating Pseudo- Boolean Constraints into SAT, Journal on Satisfiability, Boolean Modeling and Computation 2:1-25,

27 Thank You! 27

28 28

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