Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datasets. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of the performance levels achieved by deep neural networks. To improve performance, ensemble methods are used to combine multiple base learners. Super learning is an ensemble that finds the optimal combination of diverse learning algorithms. This paper proposes deep super learning as an approach which achieves log loss and accuracy results competitive to deep neural networks while employing traditional machine learning algorithms in a hierarchical structure. The deep super learner is flexible, adaptable, and easy to train with good performance across different tasks using identical hyper-parameter values. Using traditional machine learning requires fewer hyper-parameters, allows transparency into results, and has relatively fast convergence on smaller datasets. Experimental results show that the deep super learner has superior performance compared to the individual base learners, single-layer ensembles, and in some cases deep neural networks. Performance of the deep super learner may further be improved with task-specific tuning.

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The broad acceptance and use of Open Source Software (OSS) has underscored the necessity of investigating the means of assuring their quality. With the aim of identifying an OSS test process, three well-known OSS projects, namely Apache HTTP server, Mozilla Web browser, and NetBeans IDE were studied. In these studies, three activities were found similar to the activities of the ISO/IEC Test Process Standard. However, major differences were observed in tasks related to each of the test process activities. To systematize the OSS test process, an Open Source Software Test Process Framework (OSS-TPF) is proposed. The alignment of OSS-TPF with the ISO/IEC Test Process Standard is illustrated.

The development of open source software (OSS), and their deployment by general public as well as by different types of organizations, has increased manifold over the past decade or so. In spite of the ubiquity of OSS, the quality of many OSS remains questionable. Testing provides a curative approach for OSS quality assurance, and a comprehensive approach to testing is a knowledge-intensive endeavor. The management of knowledge in the OSS test process forms a perpetual cycle of creation, dissemination, and acquisition of test knowledge.