1. A Use Case Based Approach to Feature Models’ Construction Bo Wang, Wei Zhang, Haiyan Zhao, Zhi Jin, Hong Mei Key Laboratory of High Confidence Software Technology Ministry of Education of China Institute of Software, School of EECS Peking University

2. 2 Agenda  Background  Outline of our approach  Details of our approach  Two metamodels  A semi-automatic approach  Conclusion and Future Work

3. 3 Background  The construction of feature models  requires analysts’ good knowledge and experience on the domain.  involves quantitative analysis of various documents. Use Cases Req Spe User’s Book Feature Model Analyze Analysts Use Cases

4. 4 Agenda  Problems and Motivation  Outline of our approach  Details of our approach  Two metamodels  A semi-automatic approach  Conclusion and Future Work

5. 5 Outline of Our Approach Input Outputs AFM : Application Feature Model DFM : Domain Feature Model Step 1 Step 2 Step 3

6. 6 Outline of Our Approach : An Example UC 1 UC 2 UC n … System 1 : System 2 : System n : AFM 1 AFM 2 AFM n … System 1 : System 2 : System n : DFM AAFM 2 AAFM n Web Store Domain System 2 : System n : AAFM 1 System 1 : UC 1 —UC n : Describe how to place an order in a web store. DFM : Represents partial characteristics of the domain which is related to placing an order … Construct AFMs Merge AFMs Adjust AFMs Web Store Domain

7. 7 Agenda  Problems and Motivation  Outline of our approach  Details of our approach  Two metamodels  A semi-automatic approach  Conclusion and Future Work

8. 8 The Metamodel of Use Cases

9. 9

10. 10 The Metamodel of Feature Models  A Feature Model = Features + Relationships Refinements + Constraints

11. 11 The Metamodel of Feature Models

12. 12 Step 1 : Construct AFMs AFM : Application Feature Model DFM : Domain Feature Model Step 1 Input Output

13. 13 Step 1 : Construct AFMs  Identify  features  relationships between features from a use case by applying a set of heuristic rules.  Heuristic rules:  Two feature discovery rules  Six relationship discovery rules

14. 14 Feature Discovery Rules  The operation on each object in an action is regarded as a feature.  The method of an operation is regarded as a feature.

15. 15 Relationship Discovery Rules  Discover decomposition relationships by identifying features whose names reference the same object.

16. 16 Relationship Discovery Rules  Discover decomposition relationships by identifying the “Is-a” relationship between objects.

17. 17 Construct AFMs

18. 18 Step 2 : Adjust AFMs AFM : Application Feature Model DFM : Domain Feature Model Step 2 Input Output

19. 19 Step 2 : Adjust AFMs  Adjust the AFMs to eliminate improper features and relationships with the help a set of heuristic rules.  Three primitive adjusting operations:  Accept  Reject  Create  Heuristic rules:  Six adjusting suggestion rules  Four conflicts checking rules

20. 20  Help analyst adjust AFMs by applying six adjusting suggestion rules. Adjust AFMs AFM 1 AFM 2 AFM m AAA AFM n A Adjusted-AFMs BCBCBCBC √ √ √ √ √C√C √C√C √C√C √C√C

21. 21 Adjust AFMs  Check the conflicts among AFMs by applying four conflicts checking rules. AFM 1 AFM 2 AFM m AAA AFM n A Adjusted-AFMs × √ √ √

22. 22 Adjust AFMs  The adjusted AFM is show as follows:

23. 23 Step 3 : Merge Adjusted-AFMs AFM : Application Feature Model DFM : Domain Feature Model Step 3 Input Output

24. 24 Merge Adjusted-AFMs  The commonality and variability between the applications are found in the process of merging.  The merging algorithm is based on our previous work Chen [RE 05].

25. 25 Merge Adjusted-AFMs  The DFM is shown as follows:

26. 26 Conclusion & Future Work  Contributions:  Two rules to identify features from use cases  Six rules to identify relationships from use cases  Ten rules to help adjust AFMs  A semi-automatic process  Future work:  Find more rules to identify features and relationships  Apply the approach to more domains

27. 27 Thank you!