There and Back Again Motivation Sample Spaces and Feature Models: - PowerPoint PPT Presentation

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . Software Product Line Conference 2008 There and Back Again Motivation Sample Spaces and Feature Models: . . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 1/25 Krzysztof Czarnecki 1 Steven She 1 Andrzej Wąsowski 2 1 University of Waterloo, Canada 2 IT University of Copenhagen, Denmark

Outline start encourages stop Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . Configuration Sample Set of Configurations . . . Feature Model with Soft Constraints init encourages destroy stop encourages start destroy encourages init Motivation . . Sample Spaces Feature Models Overview . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 2/25

Outline start encourages stop Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . Configuration Sample Set of Configurations . . . Feature Model with Soft Constraints init encourages destroy stop encourages start destroy encourages init Motivation . . Sample Spaces Feature Models Overview . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 2/25

Outline start encourages stop Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . Configuration Sample Set of Configurations . . . Feature Model with Soft Constraints init encourages destroy stop encourages start destroy encourages init Motivation . . Sample Spaces Feature Models Overview . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 2/25

Outline Semantics of Soft Constraints Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . 5 Conclusions Mining on Applets 4 Application: Feature Model Mining 3 Configuration Joint Probability Distributions 2 Probabilistic Feature Models Motivation 1 Motivation Outline . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 3/25

Outline Semantics of Soft Constraints Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . 5 Conclusions Mining on Applets 4 Application: Feature Model Mining 3 Configuration Joint Probability Distributions 2 Probabilistic Feature Models Motivation 1 Motivation Outline . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 4/25

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . among legal configurations. Feature Models… automatic Motivation drive by wire . Basic Feature Models . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 5/25 → • represent commonality and variability in a product line. • describe a set of legal configurations . • But … existing feature models can not express preference

Outline Motivation Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . but some may violate it. …a constraint that should be satisfied by most configurations, Probabilistic Feature Models add soft constraints . automatic given North America automatic given gear automatic 6/25 drive by wire . . Probabilistic Feature Models (PFMs) . Conclusions Feature Model Mining Configuration Probabilistic Feature Models → [ 20 % ] [ 80 % ]

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . automatic given North America automatic given gear automatic drive by wire . . Motivation . . . . Interactive Configuration . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 7/25 → [ 20 % ] [ 80 % ]

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . automatic given North America automatic given gear automatic drive by wire Probabilistic view of the PFM. . . Motivation . . . . . Interactive Configuration . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 7/25 → [ 20 % ] [ 80 % ]

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . automatic given North America automatic given gear automatic drive by wire We begin by selecting Car and Gear . . . . Motivation . . . . . . Interactive Configuration . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 7/25 → [ 20 % ] [ 80 % ]

Outline Motivation Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . automatic given North America automatic given gear automatic drive by wire Next, we select for North America . . . . . . . . . . . . Interactive Configuration . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 7/25 → [ 20 % ] [ 80 % ]

Outline . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . automatic given North America automatic given gear automatic drive by wire Now, we select automatic . . . . . Motivation . . . . . . . Interactive Configuration . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 7/25 → [ 20 % ] [ 80 % ]

Outline Semantics of Soft Constraints Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . 5 Conclusions Mining on Applets 4 Application: Feature Model Mining 3 Configuration Joint Probability Distributions 2 Probabilistic Feature Models Motivation 1 Motivation Outline . Conclusions Feature Model Mining Configuration Probabilistic Feature Models 8/25

Outline Motivation Probabilistic Feature Models Configuration Feature Model Mining Conclusions . Semantics of Basic Feature Models drive by wire automatic The semantics of a basic feature model… is defined as a conjunction of it’s hard constraints as a propositional formula . Czarnecki, She, Wąsowski. Sample Spaces and Feature Models: There and Back Again . 9/25 →