1. Sam Malek Department of Computer Science George Mason University Dagstuhl Seminar on Software Engineering for Self-Adaptive Systems

2. 2 Quality Objectives Architectural Models Analytical Models Monitor Analyze Plan Execute Problems in practice Unwieldy for use, as software engineers are expected to construct complex analytical models Rigid analytical models cannot handle unanticipated events Analysis is computationally expensive

3. FUSION addresses these challenges through machine learning Learning is made possible through feature-oriented adaptation 3 Quality Objectives Feature Model Software Architecture

4. Design-time learning Construct functions that estimate the impact of features on quality goals Run-time learning Refine the functions due to (unanticipated) changes in the system 4 Response time = 1.553 F 1 0.673 F 2 + 0.709 F 3 + 0.163 F 1 F 3 0.843 Learning also improves the complexity of analysis by removing the irrelevant features

5. Configuration space at the feature-level is significantly smaller than the architectural-level A feature often has the same general (positive/negative) effect on a given quality objective 5

6. Unanticipated indexing problem in the travel reservation database results in a new response time pattern 6

7. Interested in the details: A. Elkhodary, N. Esfahani, and S. Malek. FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems. 18 th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2010), Santa Fe, NM, Nov. 2010. Available for download from: http://cs.gmu.edu/~smalek/papers/FSE2010.pdfhttp://cs.gmu.edu/~smalek/papers/FSE2010.pdf 7