Marcén, A. C., Lapeña, R., Pastor, Ó., & Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software, 163, 110519. doi:10.1016/j.jss.2020.110519
Pérez, F., Font, J., Arcega, L., & Cetina, C. (2019). Collaborative feature location in models through automatic query expansion. Automated Software Engineering, 26(1), 161-202. doi:10.1007/s10515-019-00251-9
ZHUANG, X., ENGEL, B. A., LOZANO-GARCIA, D. F., FERNÁNDEZ, R. N., & JOHANNSEN, C. J. (1994). Optimization of training data required for neuro-classification. International Journal of Remote Sensing, 15(16), 3271-3277. doi:10.1080/01431169408954326
[+]
Marcén, A. C., Lapeña, R., Pastor, Ó., & Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software, 163, 110519. doi:10.1016/j.jss.2020.110519
Pérez, F., Font, J., Arcega, L., & Cetina, C. (2019). Collaborative feature location in models through automatic query expansion. Automated Software Engineering, 26(1), 161-202. doi:10.1007/s10515-019-00251-9
ZHUANG, X., ENGEL, B. A., LOZANO-GARCIA, D. F., FERNÁNDEZ, R. N., & JOHANNSEN, C. J. (1994). Optimization of training data required for neuro-classification. International Journal of Remote Sensing, 15(16), 3271-3277. doi:10.1080/01431169408954326
Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335-1343. doi:10.1109/tgrs.2004.827257
Foody, G. M., Mathur, A., Sanchez-Hernandez, C., & Boyd, D. S. (2006). Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104(1), 1-14. doi:10.1016/j.rse.2006.03.004
Weiss, G. M., & Provost, F. (2003). Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research, 19, 315-354. doi:10.1613/jair.1199
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. doi:10.1016/j.neunet.2018.07.011
Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9
Lapeña, R., Font, J., Pastor, Ó., & Cetina, C. (2017). Analyzing the impact of natural language processing over feature location in models. ACM SIGPLAN Notices, 52(12), 63-76. doi:10.1145/3170492.3136052
Shabtai, A., Moskovitch, R., Elovici, Y., & Glezer, C. (2009). Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Information Security Technical Report, 14(1), 16-29. doi:10.1016/j.istr.2009.03.003
Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356-370. doi:10.1109/tse.2010.90
Cao, Z., Tian, Y., Le, T.-D. B., & Lo, D. (2018). Rule-based specification mining leveraging learning to rank. Automated Software Engineering, 25(3), 501-530. doi:10.1007/s10515-018-0231-z
Arcuri, A., & Briand, L. (2012). A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability, 24(3), 219-250. doi:10.1002/stvr.1486
García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044-2064. doi:10.1016/j.ins.2009.12.010
Falessi, D., Di Penta, M., Canfora, G., & Cantone, G. (2016). Estimating the number of remaining links in traceability recovery. Empirical Software Engineering, 22(3), 996-1027. doi:10.1007/s10664-016-9460-6
Jialei Wang, Peilin Zhao, Hoi, S. C. H., & Rong Jin. (2014). Online Feature Selection and Its Applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. doi:10.1109/tkde.2013.32
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