Sam Supakkul -- RE10.. - The University of Texas at Dallas

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A A selection selection pattern pattern captures captures an an an application application application independent independent independent selection selection selection scheme scheme scheme Weight-based quantitative selection alternatives 5 6 7 234 1 w(!)=0.5 ! Trustworthiness [Security] w(+)=0.5 w(++)=1.0 + ++ Retina authentication Fingerprint authentication w=0.25+... w=0.5+... ! Trustworthiness [Security] r(+!)=7 r(++!)=6 + ++ Retina authentication Fingerprint authentication r=7+... r=6+... Rank Rank-based Rank based qualitative selection Weight Weight-based Weight Weight based Selection Selection = Highest Highest cumulative weight weight weight(Fingerprint) = w(!Trust.) x w(+) + … = 0.25 + … Widely used, but subjective Rank-based Selection = Best est cumulative ranking ranking rank(Fingerprint) = r(+!Trust.) + … = 7 + … Less subjective, but need a ranking scale
Before After Demo Demo video: video: applying applying applying a a a weight weight-based weight weight based based selection selection pattern pattern pattern W+ W+ Tools used The NFR Pattern Assistant (utdallas.edu/~supakkul/tools/NFRPassist) The RE-Tools (utdallas.edu/~supakkul/tools/RE-Tools)
Page 1 and 2: An An NFR NFR Pattern Pattern Appro
Page 3 and 4: Some Some aspects aspects of of NFR
Page 5 and 6: Having Having Having Having suffici
Page 7 and 8: 4 4 kinds kinds of of NFR NFR patte
Page 9 and 10: In the catalog Demo Demo Demo video
Page 11: An An alternatives alternatives pat
Page 15 and 16: Generalization Aggregation Classifi
Page 17 and 18: Generalization Aggregation Classifi
Page 19 and 20: Dealing Dealing Dealing with with N
Page 21 and 22: We We define define define 25 25 re
Page 23 and 24: The The The The NFR NFR NFR NFR NFR
Page 25 and 26: Limitations Limitations (future (fu
Page 27: Thank you… Questions & Comments?

Sam Supakkul -- RE10.. - The University of Texas at Dallas

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