MODEL-BASED DEVELOPMENT: THEORY AND PRACTICE

This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation. You will learn: Understand algorithmic test generation techniques and their use in developer testing and continuous integration. Understand how to automatically generate test cases with assertions. Have a working knowledge and experience in static and dynamic generation of tests. Have an overview knowledge in search-based testing and the use of machine learning for test generation.

This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.

The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.

The purpose is to give the students an overview of issues and methods for development and assurance of safety-critical software, including details of selected technologies, methods and tools. The course includes four modules: Introduction to functional safety; knowledge that give increased understanding of the relationship between Embedded systems / safety-critical system / accidents / complexity / development models (development lifecycle models) / certification / “the safety case”. Analysis and modelling methods; review of analysis and modelling techniques for the development of safety-critical systems. Verification and validation of safety critical software, methods and activities to perform verification and validation. Architectures for safety critical systems. Safety as a design constraint.  

In this course, you will be made aware of the state-of-the-art in cybersecurity research and state of practice in industry. Cybersecurity vulnerabilities are a threat to progress in the business sector and society. This is an accelerating threat due to the current rapid digitalisation, which in manufacturing is termed Industry 4.0. Companies are aware of this threat and realise the need to invest in countermeasures, but development is hampered by lack of competence.  

Fiber-optic sensing technologies are fast evolving and have entered in a large domain of our industry. Today all geothermal fields, water dams, railroads and to some lesser extent mines are equipped with fiber-optic cables to allow not only digital data transmission but also to interrogate fiber cables for information such as temperature changes or values (leakage issues or fractured rocks) but also strain measurements that can be indicators of soil failure or movements. When conducted in a controlled manner, artificial signals can be generated to help image the subsurface for mineral exploration, mine tailing characterization and for geothermal field development work by mapping faults and thermal fluids. There are other applications such as traffic monitoring that can also be done using this technology. Given its vast applications in the green transition, fiber-optic sensing is one of the most advanced technologies to be implemented in a wide range of fossil-free energy systems, hence, of a great importance to learn about their pros and cons and possibilities. Course content The course will have the following content: Introduction to DAS DAS Interrogators for temperature and strain measurements Fiber optic cables and their health conditions (hands-on with fiber-cable microscopes and fusion splicers) Design of a fiber-optic survey (surface and borehole) Parameter testing such as gauge length, laser pulse and width Field trials at a mine tailing test site or a mineral exploration borehole Work with the data and reporting Course design Hybrid and blended including hands-on practices.  This course takes about 30 hours of study to complete. You will learn By taking the course the participants are intended to learn about: Fiber-optic cables and their specifications including how to check their health and splice them DAS interrogators and their interior designs for fiber-optic sensing applications Design surface and borehole experiments Read and work with the data (hands-on) Who is the course for? The course will be given to a broad range of participants from engineering to geoscience backgrounds including university students but also participants from the industry. Participants can be from construction industry, road administration, energy sector (e.g., water dams), mining and defence workers. The course will be run within the newly established Smart Exploration Research Center involving tech companies such as BitSimNow Part of Prevas who are also expert in PFGA and fiber-related technologies. A prerequisite to the course is prior knowledge on different problems in the energy sector but some knowledge with Matlab and/or Python programming. The course can continue as an industry offer through the SERC-center as a multidisciplinary course at Uppsala University and for industry participants.