Abstract
Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, especially with the application of machine learning techniques to help identify risk levels of risk factors of a project before its development begins, with the goal of improving the likelihood of success of these projects. This paper presents the results of the application of machine learning techniques for risk assessment in software projects. A Python application was developed and, using Scikit-learn, two machine learning models, trained using software project risk data shared by a partner company of this project, were created to predict risk impact and likelihood levels on a scale of 1 to 3. Different algorithms were tested to compare the results obtained by high performance but non-interpretable algorithms (e.g., Support Vector Machine) and the ones obtained by interpretable algorithms (e.g., Random Forest), whose performance tends to be lower than their non-interpretable counterparts. The results showed that Support Vector Machine and Naive Bayes were the best performing algorithms. Support Vector Machine had an accuracy of 69% in predicting impact levels, and Naive Bayes had an accuracy of 63% in predicting likelihood levels, but the results presented in other evaluation metrics (e.g., AUC, Precision) show the potential of the approach presented in this use case.

Abstract
One of the most critical ICT application domains is healthcare, where a single failure can lead a patient into a hazardous situation. Due to this, there's a great necessity to ensure that the developed solutions are safe and secure and perform as expected. Smart-Health-4-All (SH4ALL) is a project aiming at accelerating the research, development, commercialization, and dissemination of trustworthy smart health solutions in Portugal. One of the key components of the project is a web platform that supports the generation of integration and system tests for smart health solutions (comprising medical devices, applications, etc.), following a software product line approach. At the domain engineering level, the platform supports the creation of feature models and related test patterns for families of smart health products. At the product engineering level, the platform supports the instantiation of test patterns and the generation of corresponding test scripts ready for execution on specific products under test. This paper presents the aforementioned test platform and test process, and the discovery of test patterns.

Abstract
Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

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Abstract
Adaptive Object Models, though a well-known architectural pattern, is seldomly used in software projects where, due to their nature, would highly benefit from it. Characteristics such as complexity, reduced literature and case-studies, lack of reusable framework components, and fundamental issues as those regarding runtime evolution, drive developers away. By overcoming these barriers with a set of patterns, tools and applications, and addressing pending research problems, Adaptive Object Models can dramatically alter the way developers design their software. This paper presents a survey in the field, describes the preliminary contributions and outlines the ongoing doctoral work.