Despite the conventional wisdom concerning the risks related to the use of source code cloning as a software development strategy, several studies appeared in literature indicated that this is not true. In most cases clones are properly maintained and, when this does not happen, is because cloned code evolves independently. Stemming from previous works, this paper combines clone detection and co-change analysis to investigate how clones are maintained when an evolution activity or a bug fixing impact a source code fragment belonging to a clone class. The two case studies reported confirm that, either for bug fixing or for evolution purposes, most of the cloned code is consistently maintained during the same co-change or during temporally close co-changes

In the last years, the alignment issue was addressed in several researches and numerous methods, techniques and tools were proposed. Indeed, the business and IT performance are tightly coupled, and enterprises cannot be competitive if their business and IT strategies are not aligned. This paper proposes a literature review useful for evaluating different alignment approaches, with the aim of discovering similarity, maturity, capability to measure, model, asses and evolve the alignment level existing among business and technological assets of an enterprise. The proposed framework is applied to analyse the alignment research published in the Information & Management journal and the Journal of Strategic Information Systems, that are the ones that more published on this topic. The achieved evaluation results are presented

Anomaly detection in network traffic is a hot and ongoing research theme especially when concerning IoT devices, which are quickly spreading throughout various situations of people’s life and, at the same time, prone to be attacked through different weak points. In this paper, we tackle the emerging anomaly detection problem in IoT, by integrating five different datasets of abnormal IoT traffic and evaluating them with a deep learning approach capable of identifying both normal and malicious IoT traffic as well as different types of anomalies. The large integrated dataset is aimed at providing a realistic and still missing benchmark for IoT normal and abnormal traffic, with data coming from different IoT scenarios. Moreover, the deep learning approach has been enriched through a proper hyperparameter optimization phase, a feature reduction phase by using an autoencoder neural network, and a study of the robustness of the best considered deep neural networks in situations affected by Gaussian noise over some of the considered features. The obtained results demonstrate the effectiveness of the created IoT dataset for anomaly detection using deep learning techniques, also in a noisy scenario.