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Change-prone modules are more likely to produce defects and accumulate technical debt. Thus, developing prediction models for determining change-prone software classes is critical. Such models will allow for more efficient resource utilization during the maintenance phase and will make them more adaptable to future changes. This paper applies the study on a large dataset from a commercial software to investigate the relationships between object-oriented metrics and change-proneness. The study also compared the performance of several machine learning techniques including combining methods that were constructed by combining several single and ensemble classifiers with voting, Select-Best, and stacking scheme. The result of the study indicates a high prediction performance of many of the ensemble classifiers and the combining methods selected and proved that machine learning methods are very beneficial for predicting change-prone classes in software. The study also demonstrated that software metrics are significant indicators of class change-proneness and should be monitored regularly.

Evidence based on ongoing published research shows that timetabling has been a challenge for over two decades. There is a growing need in higher education for a learner-centered solution focused on individual preferences. In the authors' earlier published work, students' group assessment information was mined to determine individualized achievements and predict future performance. In this paper, they extend the work to present a solution that uses students' individualized achievements, expected future performance, and historical registration records to discover students' registration timing patterns, as well as the most appropriate courses for registration. Such information is then processed to build the most suitable timetable for each student in the following semester. Faculty members' time preferences are also predicted based on historical teaching time patterns and course teaching preferences. The authors propose a modified frequent pattern (FP)-tree algorithm to process the predicted information. This results in clustering students to solve the timetable problem based on the predicted courses for registration. Then, it divides the timetable problem into subproblems for resolution. This ensures that time will not conflict within the generated timetables while satisfying both the hard and soft constraints. Both students' and faculty members timetabling preferences are met (88.8% and 85%).