From http://www.splot-research.org/
INPUT:
Important:<feature_model name="My feature model"> <-- feature model start tag and name attribute (mandatory) <meta> <-- Optional <data name="description">Model description</data> <-- Optional <data name="creator">Model's creator</data> <-- Optional <data name="email">Model creator's email</data> <-- Optional <data name="date">Model creation date</data> <-- Optional <data name="department">Model creator's department</data> <-- Optional <data name="organization">Model creator's organization</data> <-- Optional <data name="address">Model creator's address</data> <-- Optional <data name="phone">Model creator's phone</data> <-- Optional <data name="website">Model creator's website</data> <-- Optional <data name="reference">Model's related publication</data> <-- Optional </meta> <feature_tree> <-- feature tree start tag :r root (root_id) <-- root feature named 'root' with unique ID 'root_id' :o opt1 (id_opt1) <-- an optional feature named opt1 with unique id id_opt1 :o opt2 (id_opt2) <-- an optional feature named opt2, child of opt1 with unique id id_opt2 :m man1 <-- an mandatory feature named man1 with unique id id_man1 :g [1,*] <-- an inclusive-OR feature group with cardinality [1..*] ([1..3] also allowed) : a (id_a) <-- a grouped feature name a with ID id_a : b (id_b) <-- a grouped feature name b with ID id_b :o opt3 (id_opt3) <-- an optional feature opt3 child of b with unique id id_opt3 : c (id_c) <-- a grouped feature name c with ID id_c :g [1,1] <-- an exclusive-OR feature group with cardinality [1..1] : d (id_d) <-- a grouped feature name d with ID id_d : e (id_e) <-- a grouped feature name e with ID id_e :g [2,3] <-- a feature group with cardinality [2..3] children of feature e : f (id_f) <-- a grouped feature name f with ID id_f : g (id_g) <-- a grouped feature name g with ID id_g : h (id_h) <-- a grouped feature name h with ID id_h </feature_tree> <-- feature tree end tag (mandatory) <constraints> <-- extra constraints start tag (mandatory) c1: ~id_a or id_opt2 <-- extra constraint named c1: id_a implies id_opt2 (must be a CNF clause) c2: ~id_c or ~id_e <-- extra constraint named c2: id_c excludes id_e (must be a CNF clause) </constraints> <-- extra constraint end tag (mandatory) </feature_model> <-- feature model end tag (mandatory)
- Tags are optional but encouraged
- Feature tree must have at least one feature, i.e., the root node
- Extra constraints are optional
- Each extra constraint must be a CNF clause (any arity)
State of the art in checking these models:
- A Performance Comparison of Contemporary Algorithmic Approaches for Automated Analysis Operations on Feature Models
Richard Pohl, Kim Lauenroth and Klaus Pohl. IEEE ASE 2011
also; just says "no" and maybe offers one counter example
here, a linear-time greedy bayes search (think simpler than W2) generates this in 6 seconds in awk from 10,000 random models from a 300+ class system
Output:
MEDIAN N treatment
------ --- ---------
[ ------ | ------ ] 80 | 10000 all rows
[ ----| --- ] 85 | 4945 _id_35 = 1
[ --- |-- ] 90 | 2521 _id_32 = 1
[ --- |-- ] 90 | 2213 _id_68 = 0
[ --- |-- ] 90 | 1903 _id_63 = 0
[ --- |-- ] 90 | 1427 _id_77 = 0
[ --- |-- ] 90 | 711 _id_15 = 1
[ --- | - ] 90 | 355 _id_17 = 1
[ --- | - ] 90 | 170 _id_22 = 1
[ --- | - ] 90 | 86 _id_86 = 0
[ --- |-- ] 90 | 47 _id_24 = 1
[ -----| ----] 90 | 20 _id_3 = 0
What's fun here is that it is telling us what to do and what not to do.
