Generation of an Architecture View for Web Applications using a Bayesian Network Classifier

Session: Software EngineeringInternational audienceA recurring problem in software engineering is the correct definition and enforcement of an architecture that can guide the development and maintenance processes of software systems. This is due in part to a lack of correct definition and maintenance of architectural documentation. In this paper, an approach based on a bayesian network classifier is proposed to aid in the generation of an architecture view for web applications developed according to the Model View Controller (MVC) architectural pattern. This view is comprised of the system components, their inter-project relations and their classification according to the MVC pattern. The generated view can then be used as part of the system documentation to help enforce the original architectural intent when changes are applied to the system. Finally, an implementation of this approach is presented for Java based-systems, using training data from popular web development frameworks

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Behaviour analysis through Machine learning techniques

Behaviour analysis is the science of studying the comportment of a person to establish a specific profile about it. It has firstly been used in psychology and since a few years, it has been implemented in information technology programs to improve and suggest in different forms the content of an application for users. With the growth of artificial intelligence, it tends to become the new trend that gives the possibility for applications to be personalized and centred on the user’s needs. Machine Learning is a subcategory of artificial intelligence and has the goal to develop solutions to implement automatic methods to make our computers capable of evolving by themselves. The activities and actions of users start to be analysed to determine rules that can be integrated to align software applications in parallel with the daily routine and comportment of a person. This thesis is part of a healthcare mobile application project (mHealth) that has for objectives to develop a management tools for the medical personal to administer the patients in the hospital. Moreover, this application would like to use the location of a user and his habits of utilisation of the software, to quickly provide information for the nurse and therefore, reduce human-machine interaction and save precious time for better purposes. These goals are starting to be feasible through the utilisation of correct technologies and technics. This thesis analyses the different data that can be provided and uses machine learning algorithm technics to study the behaviour of a user to predict his needs and suggest him content. Specifically, we simulate the comportment of a nurse to subsequently be construed by our machine learning solution. Thereafter, we provide the predicted content for the user via a Web Service. The solution that we have developed has a current accuracy of 75% and the model created with simulated data will progressively adjust itself with the real data in the healthcare environment