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Dynamic Software Project Scheduling through a Proactive-Rescheduling Method.

, , , and . IEEE Trans. Software Eng., 42 (7): 658-686 (2016)

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Measuring Energy Consumption for Web Service Product Configuration., , and . iiWAS, page 224-228. ACM, (2014)Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis., , , and . Soft Comput., 20 (10): 3889-3914 (2016)How to make best use of cross-company data in software effort estimation?, and . ICSE, page 446-456. ACM, (2014)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)The impact of parameter tuning on software effort estimation using learning machines., , and . PROMISE, page 9:1-9:10. ACM, (2013)Using unreliable data for creating more reliable online learners., and . IJCNN, page 1-8. IEEE, (2012)BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain., , , , and . IEEE Trans. Evol. Comput., 26 (6): 1380-1393 (2022)The impact of data difficulty factors on classification of imbalanced and concept drifting data streams., , , , and . Knowl. Inf. Syst., 63 (6): 1429-1469 (2021)Dynamic Evaluation of Microservice Granularity Adaptation., , , and . ACM Trans. Auton. Adapt. Syst., 16 (2): 4:1-4:35 (2021)