Automatic test definition for high-integrity systems

A atividade de testes é uma das tarefas mais dispendiosas no ciclo de vida de desenvolvimento de software. No sentido de otimizar o esforço gasto nestas tarefas, foi desenvolvida uma ferramenta, Sesnando, cujo objectivo é interpretar e compilar requisitos de sistema escritos numa linguagem natural controlada e a partir destes gerar automaticamente um conjunto de testes que permitam verificar a implementação destes mesmos requisitos. Durante a fase de interpretação do requisito, o Sesnando age como um validador da sua escrita e fornece mensagens ao utilizador sobre a sua construção. Posteriormente, gera um conjunto de testes para a sua verificação. Neste trabalho, é também feita uma avaliação sobre as capacidades do Sesnando assim como uma análise relativamente aos métodos tradicionais. Os resultados obtidos mostram que é possível reduzir o esforço na atividade de especificação de testes de sistema em até 90%

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Android Malware Detection Using Category-Based Machine Learning Classifiers

Android malware growth has been increasing dramatically along with increasing of the diversity and complicity of their developing techniques. Machine learning techniques are the current methods to model patterns of static features and dynamic behaviors of Android malware. Whereas the accuracy rates of the classifiers increase with increasing the quality of the features, we relate between the apps\u27 features and the features that are needed to deliver the category\u27s functionality. Differently, our classification approach defines legitimate static features for benign apps under a specific category as opposite to identifying malicious patterns. We utilize the features of the top rated apps in a specific category to learn a malware detection classifier for the given category. Android apps stores organize apps into different categories; For example, Google play store organizes apps into 26 categories such as: Health and Fitness, News and Magazine, Music and Audio, etc. Each category has its distinct functionality which means the apps under a specific category are similar in their static and dynamic features. In general, benign apps under a certain category tend to share a common set of features. On the contrary, malicious apps tend to request abnormal features, less or more than what are common for the category that they belong to. This study proposes category-based machine learning classifiers to enhance the performance of classification models at detecting malicious apps under a certain category. The intensive machine learning experiments proved that category-based classifiers report a remarkable higher average performance compared to non-category based