Regression testing is very important but also a very costly and time-consuming activity that ensures the developers that changes in the application will not bring new errors. Retest all, selection of test cases and prioritization of test cases (TCP)  approaches are used to enhance the efficiency and effectiveness in regression testing. While test case selection techniques decrease testing time and cost, it can exclude some critical test cases that can detect the faults. On the other hand, test case prioritization considers all test cases and execute them until resources are exhausted or all test cases are executed, while always focusing on the most important ones. Over the years, machine learning has found wide usage in solving different problems in software engineering. Software development and maintenance problems can be defined as learning problems and machine learning techniques have shown to be very effective in solving these problems. In the range of application of machine learning, machine learning techniques have also found usage in solving the test case prioritization problem. In this paper, we investigate the application of machine learning techniques in test case prioritization. We survey some of the most recent studies made in this field and provide information like techniques of machine learning used in TCP process, metrics used to measure the effectiveness of the proposed methods, data used to define the priority of test cases and some advantages or limitations of application of machine learning in TCP.

Higher Education Institutions (HEIs) are involved in an evolution to a new model of university called digital university. This model implies not only adopting new technologies but also developing an organizational strategic transformation which includes information, processes, human aspects, and more. Because an organization’s digital maturity correlates with the scope of its digital transformation efforts, this study aims to identify digital transformation initiatives (DTI) taken by HEIs, defining the new processes and technologies used to implement them. The main motivation is to have a real and clear vision of how universities are transforming themselves, discovering the most relevant DTI that they have applied and if they are doing it through an integrated plan aligned with a digital strategy, as recommended by experts. We conducted a Multivocal Literature Review, as methodology research, to include both academic and grey literature in the analysis. Main results show that the DTI implemented are primarily focused on providing a quality and competitive education (24% of 184 DTI from 39 different universities analyzed). Emerging technologies most frequently used are advanced analytics (23%), cloud (20%) and artificial intelligence (16% of total DTI). We conclude that HEIs are in the first steps to digital maturity as only 1 in 4 have a digital strategy and 56% have launched isolated DTI that are not integrated in a plan and do not have a high strategic return value to the organization.

In this extended abstract, we propose an Artificial Intelligence-based model dedicated to the representation of a multi-class traffic flow, i.e. a traffic flow in which different vehicle classes (at least cars and trucks) are explicitly represented, with the aim of using it for the development of freeway traffic control schemes based on ramp management. Specifically, the goal of this work is to develop a hybrid modelling technique in which a Machine Learning component and the multi-class version of METANET model are adopted to determine a better estimation and forecasting tool for freeway traffic. The resulting model is specifically devised in order to be included in a Model Predictive Control (MPC) scheme for the required traffic state prediction.

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