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Welcome to the 11th international conference on predictive models and data analytics in software engineering (PROMISE 2015)

, , , , and . 2015-October, (2015)

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Welcome to the 10th International Conference on Predictive Models in Software Engineering (PROMISE 2014), , , , , and . (2014)Welcome to the 12th international conference on predictive models and data analytics in software engineering (PROMISE 2016), , , , , , and . (2016)Welcome to the 11th international conference on predictive models and data analytics in software engineering (PROMISE 2015), , , , and . 2015-October, (2015)4th International Workshop on Realizing AI Synergies in Software Engineering (RAISE 2015), , , , , and . 2, (2015)Message from the workshop chairs, , and . (2016)Ensembles and locality: Insight on improving software effort estimation., and . Inf. Softw. Technol., 55 (8): 1512-1528 (2013)GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts., , and . IJCNN, page 1-8. IEEE, (2019)Using unreliable data for creating more reliable online learners., and . IJCNN, page 1-8. IEEE, (2012)How to make best use of cross-company data in software effort estimation?, and . ICSE, page 446-456. ACM, (2014)Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis., , , and . Soft Comput., 20 (10): 3889-3914 (2016)