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A Report on the Fifth Workshop on Emerging Software Engineering Education (WESEE 2023)., , and . ISEC, page 28:1-28:2. ACM, (2023)An Approach for the Prediction of Number of Software Faults Based on the Dynamic Selection of Learning Techniques., and . IEEE Trans. Reliability, 68 (1): 216-236 (2019)Software Fault Prediction - A Road Map, and . Springer Briefs in Computer Science Springer, (2018)Analysis of Use Case Requirements Using SFTA and SFMEA Techniques., , , , and . ICECCS, page 29-38. IEEE Computer Society, (2012)Open-world Machine Learning: Applications, Challenges, and Opportunities., , and . CoRR, (2021)Linear and non-linear bayesian regression methods for software fault prediction., and . Int. J. Syst. Assur. Eng. Manag., 13 (4): 1864-1884 (2022)Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems., and . Knowl. Based Syst., (2017)A Report on the Fourth Workshop on Emerging Software Engineering Education (WESEE 2021)., , and . ISEC, page 26:1-26:2. ACM, (2021)Authorship Identification Using Stylometry and Document Fingerprinting., , and . BDA, volume 12581 of Lecture Notes in Computer Science, page 278-288. Springer, (2020)Ensemble methods for the prediction of number of faults: A study on eclipse project., and . ICIIS, page 540-545. IEEE, (2016)