Feature Location in Models (FLiMEA) Universidad San Jorge 1 FLiMEA - PowerPoint PPT Presentation

REVAMP 2 Feature Location in Models (FLiMEA) Universidad San Jorge 1

FLiMEA REVAMP 2 What? Why? Where? How? Feature Location Problem Feature Description Ranking of Model Fragments Solution: Model Fragments Search Space: Model FLiMEA 2

FLiMEA REVAMP 2 What? Why? Where? How? Why is Feature Location important? 3

FLiMEA REVAMP 2 What? Why? Where? How? Textual modeling Grafcet UML Class Diagram BPMN Entity Relationship 4

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FLiMEA REVAMP 2 What? Why? Where? How? Learning to Rank is the name given to a family of Machine Learning algorithms, which automatically address ranking tasks. Individual Individual Individual Individuals Individual Individual Ranking 8

FLiMEA REVAMP 2 What? Why? Where? How? Individual Individual Individual Individuals Individual Individual Classifier Ranking 9

FLiMEA REVAMP 2 What? Why? Where? How? Individual Individual Individual Feature Individuals Individual Individual Classifier Vectors Ranking 10

FLiMEA REVAMP 2 What? Why? Where? How? Ideal house Under construction house 11

FLiMEA REVAMP 2 What? Why? Where? How? Individual Individual Model Individual Model Feature Individual Fragments Encoding Classifier Fragment Vectors Ranking 12

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FLiMEA REVAMP 2 What? Why? Where? How? Model Fragment Evolutionary Algorithm FLiMEA Learning to Rank 14

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FLiMEA REVAMP 2 What? Why? Where? How? Demo 18

Industrial experiences REVAMP 2 Induction Hobs of B/S/H/ (produced under the Bosch, Siemens, Balay, Neff, Gaggenau brands, among others) Rolling stock of CAF (Trains, Trams, High-speed, and Underground) 22

Publications REVAMP 2 ▪ JSS 2018, Improving feature location in long-living model-based product families designed with sustainability goals. ▪ MODELS 2018, Evolutionary Algorithm for Bug Location in the Reconfigurations of Models at Runtime. ▪ MODELS 2018, Measures to report the Location Problem of Model Fragment Location. ▪ CAiSE 2018, Exploring New Directions in Traceability Link Recovery in Models. ▪ GPCE 2017, Analyzing the impact of natural language processing over feature location in models. ▪ ER 2017, Ontological evolutionary encoding to bridge machine learning and conceptual models: approach and industrial evaluation. ▪ ER Forum 2017, On the Influence of Models-to Natural-Language Transformation among Requirements and Conceptual Models. ▪ REVE 2017, Towards Feature Location in Models through a Learning to Rank Approach. 23

More info REVAMP 2 Font, Jaime; Arcega, Lorena; Haugen, Øystein; Cetina, Carlos; Achieving Feature Location in Families of Models through the use of Search-Based Software Engineering. IEEE Transactions on Evolutionary computation. 2018 Marcén, Ana Cristina; Pérez, Francisca; Cetina, Carlos; Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models : Approach and Industrial Evaluation. 36th International Conference on Conceptual Modeling. 2017 Pérez, Francisca; Marcén, Ana Cristina; Lapeña, Raúl; Cetina, Carlos; Introducing Collaboration for Locating Features in Models : Approach and Industrial Evaluation. 25th International Conference on Cooperative Information Systems. 2017 24

Contact us REVAMP 2 Universidad San Jorge: Ana C. Marcén acmarcen@usj.es Carlos Cetina ccetina@usj.es Jaime Font jfont@usj.es Research Group Website: REVAMP Web Site : http://svit.usj.es http://revamp-project.eu/ 25

Thanks! REVAMP 2 26