Software testing is an essential aspect of software development, ensuring the reliability, functionality, and overall quality of software products. Integration testing, a critical phase in the testing hierarchy, verifies interactions between software components. However, teaching integration testing presents challenges due to its complexity and abstract nature. To address these issues, this study explores the use of the VARK learning model - Visual, Auditory, Reading/Writing, and Kinesthetic - to design and implement multimodal activities that cater to diverse learning styles. A cohort of students engaged in VARK-based activities aimed at improving their understanding of integration testing concepts. The results indicated improved student engagement and comprehension, with participants demonstrating a greater ability to apply theoretical knowledge in practical testing scenarios (increase correctness from 80% to 93% and from 50% to 70% for several testing concepts). Around 60% of students agreed that VARK learning created a better understanding of how to use and apply integration testing. This paper discusses the implementation of these activities, their outcomes, and the potential of VARK-based approaches to advance technical education, especially in software engineering. These findings provide insights for educators looking for innovative methods to teach complex technical concepts effectively.

Abstract Success in software projects is now an important challenge. The main focus of the engineering community is to predict software defects based on the history of classes and other code elements. However, these software defect prediction techniques are effective only as long as there is enough data to train the prediction model. To mitigate this problem, cross-project defect prediction is used. The purpose of this research investigation is twofold: first, to replicate the experiments in the original paper proposal, and second, to investigate other settings regarding defect prediction with the aim of providing new insights and results regarding the best approach. In this study, three composite algorithms, namely AvgVoting, MaxVoting and Bagging are used. These algorithms integrate multiple machine classifiers to improve cross-project defect prediction. The experiments use pre-processed methods (normalization and standardization) and also feature selection. The results of the replicated experiments confirm the original findings when using raw data for all three methods. When normalization is applied, better results than in the original paper are obtained. Even better results are obtained when feature selection is used. In the original paper, the MaxVoting approach shows the best performance in terms of the F-measure, and BaggingJ48 shows the best performance in terms of cost-effectiveness. The same results in terms of F-measure were obtained in the current experiments: best MaxVoting, followed by AvgVoting and then by BaggingJ48. Our results emphasize the previously obtained outcome; the original study is confirmed when using raw data. Moreover, we obtained better results when using preprocessing and feature selection.