This graduate-level course focuses on the advanced application of data mining and machine learning techniques to tackle complex software engineering challenges. Students will delve into the theoretical foundations and practical implementations of state-of-the-art algorithms used for software defect prediction, effort estimation, bug localization, and software quality assessment. Emphasizing real-world applications, the course introduces students to mining software repositories, processing large-scale and imbalanced datasets, and applying deep learning and natural language processing (NLP) techniques to extract insights from software engineering artifacts like code repositories, issue tracking systems, and technical documentation. Prerequisites: Admission to either graduate program in Electrical, Computer or Software Engineering or Engineering program advisor permission.
This graduate-level course focuses on the advanced application of data mining and machine learning techniques to tackle complex software engineering challenges. Students will delve into the theoretical foundations and practical implementations of state-of-the-art algorithms used for software defect prediction, effort estimation, bug localization, and software quality assessment. Emphasizing real-world applications, the course introduces students to mining software repositories, processing large-scale and imbalanced datasets, and applying deep learning and natural language processing (NLP) techniques to extract insights from software engineering artifacts like code repositories, issue tracking systems, and technical documentation. Prerequisites: Admission to either graduate program in Electrical, Computer or Software Engineering or Engineering program advisor permission.
