Technical Debt (TD) is one of the main obstacles to the quality and sustainability of software in the long term, accumulating "interest" that increases maintenance costs and reduces the productivity of development teams. Among the strategies to manage TD, refactoring stands out for its ability to improve the internal structure of the code without changing its external behavior, contributing to improving the readability, maintainability, and extensibility of the software. This study analyzes the impact of refactoring on TD using metrics extracted from SonarQube and RefactoringMiner at the class level. TD is represented by the "sqale_debt_ratio" and stratified in quartiles to model its levels. The analysis focuses both on the presence or absence of refactoring and on the influence of specific types and levels of applications. Evaluating the role of refactoring is fundamental to understanding how these practices can reduce TD, improve software quality, and optimize maintenance. To ensure the robustness of the models, metrics directly related to TD were removed and collinearity-based selection techniques were applied. The validation of the approach was performed on six open-source software systems available on GitHub. The predictive models, built with machine learning techniques, were analyzed with SHAP (Shapley Additive exPlanations) to identify the most relevant features. The results of the study are promising and demonstrate how the adoption of targeted refactoring practices can effectively reduce TD. The combined approach of prediction and explainability helps to fill a gap in the literature and offers practical guidance for managing TD and optimizing refactoring practices in software systems.

Artificial intelligence and Machine Learning represent a fundamental role in the medical field today, even in the case of neurode-generative diseases such as Parkinson’s disease; particularly in non-invasive diagnostics. This work presents a machine learning-based approach for the early detection of Parkinson’s disease through voice recordings. Additionally, explainability techniques are applied to highlight key vocal features associated with the condition, supporting clinical interpretation and future integration in digital healthcare systems. The study aims to contribute to the timely diagnosis of Parkinson’s, providing an in-depth understanding of the disease to open up new opportunities for a more personalised and precise approach to therapies, representing a first step toward the future of predictive medicine.

Parkinson’s Disease (PD) is a neurodegenerative condition primarily affecting the elderly but also occurring in younger individuals. It is caused by a progressive loss of nerve cells in the brain’s substantia nigra that release dopamine, essential for controlling movements. Dopamine deficiency results in symptoms affecting both motor and non-motor functions, which vary among individuals. Diagnosis relies on clinical symptoms and medical history, often supported by brain scans, as there is no specific diagnostic test available. Diagnosis is challenging due to vague initial symptoms resembling other conditions. Current research indicates that AI can significantly enhance data and image analysis, aiding in the diagnosis and monitoring of PD progression. To this aim, this study proposes a hybrid model allowing the integrated use of clinical data and single photon emission computed tomography images of a patient to predict the presence of the disease. The approach consists of a combination of two types of neural networks, an LSTM for clinical data and a CNN for images. The validation is performed on a widely validated dataset belonging to the Parkinson’s Progression Markers Initiative, from which the data recording visits of 1,814 patients were extracted. The obtained results are interesting and useful to address further investigations.

Today, several chest diseases are on the rise and these are often diagnosed through the use of chest X-rays, a common and economical clinical test to perform. This work uses a machine learning approach for the detection of thoracic diseases using chest X-ray reports and involves leveraging algorithms and models to analyze medical imaging data for the presence of various conditions affecting the chest area. Our main goal is to create a predictive model based on textual reports released by radiologists, with the use of Natural Language Processing. The proposed approach aims to facilitate the examination of textual reports written by radiologists, to predict the onset of diseases in patients. Specifically, reports generated by radiologists are meticulously processed and reviewed using the GloVe and LSI models. This analysis allows you to identify the presence of diseases and provides insights into the specific thoracic pathology. The results obtained through the implementation of our approach (accuracy above 96% for the best model) underline the good performance and potential of the developed predictive model.

Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.

Parkinson's disease is one of the most prevalent neurodegenerative diseases in the world, usually occurring after the age of 50, but in some cases, also affects younger people. It is a disease that affects movement, coordination, and muscle control, all of which cause a range of symptoms that affect patients' writing and drawing skills. Diagnosis is clinical, so it occurs mainly through the evaluation of the patient's movements, coordination, and muscle control. Therefore, the analysis of micrographic models can introduce a new methodology of investigation in the diagnosis and monitoring of Parkinson's disease. This study proposes an approach based on artificial intelligence in combination with the spiral test, which consists in asking the patient to draw a spiral, thanks to which it is possible to make the early diagnosis of Parkinson's disease. The classification is performed with a combination of an Echo State Network and an MLP layer. To validate the approach, several classification algorithms belonging to two macro groups (boosting decision trees based) were used as baseline. The results obtained are very satisfactory with the ESN-based classifier exhibiting an F-Score of 97.8%. The very encouraging results indicate that the proposed approach may be an effective contribution to improving Parkinson's diagnostics.

Lung cancer is one of the diseases with the highest mortality rate and early detection is key to making the disease as treatable as possible. The most common and useful method for recognizing pulmonary nodules is computed tomography, which allows them to be located and monitored. The disadvantage of this technique is that the scans have to be interpreted by doctors, who could make mistakes. The use of pulmonary CAD is therefore becoming increasingly widespread, a system capable of automatically analyzing CT images and providing information on possible suspicious regions found in the images. These systems, by offering radiologists a list of already marked regions of interest to view with particular attention, increase the efficiency of detection of small nodules and reduce reporting times by physicians. This study aims to accurately detect the location of pulmonary nodules through a Deep Learning approach with the use of computed tomography scans. In particular, it proposes the use of a new variant of the UNet architecture, called GUNet3++, which has been compared with the other types of this network. To validate the approach, the public LIDC-IDRI dataset was used, which collects pulmonary CT images of about a thousand patients with different types of cancer. The results obtained are very promising, showing a performance improvement compared to other UNet networks.

Defect prediction and estimation techniques play a significant role in software maintenance and evolution. Recently, several research studies proposed just-in-time techniques to predict defective changes. Such prediction models make the developers check and fix the defects just at the time they are introduced (commit level). Nevertheless, early prediction of defects is still a challenging task that needs to be addressed and can be improved by getting higher performances. To address this issue this paper proposes an approach exploiting a large set of features corresponding to source code metrics detected from commits history of software projects. In particular, the approach uses deep temporal convolutional networks to make the fault prediction. The evaluation is performed on a large data-set, concerning four well-known open-source projects and shows that, under certain considerations, the proposed approach has effective defect proneness prediction ability.

Technical debt is a metaphor that refers to all the consequences of poorly written code and trade-offs in development. Early technical debt diagnosis is important for software developers because it allows planning for software maintenance and improvement activities, such as refactoring, to prevent system degradation. Several studies have been conducted in the literature on the identification of the technical debt and its consequences, thanks to useful tools for identifying the problem within the source code. On the other hand, this work aims to explore a deep learning approach to predict the rise of technical debt in software code by leveraging the knowledge of changing quality metrics. For validation of the approach, a large dataset was built, related to four known Java software projects, with the collection of numerous class-level code quality metrics. The results obtained show the effectiveness of the proposed approach in predicting the development of Technical Debt within the source code. We obtained an F1 score of 0.99 for two of the chosen software systems and greater than 0.91 for the remaining two.

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists’ activities and improve the quality of the patients’ life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the ”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient’s treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%.

A significant amount of source code in software systems is made up of comments, parts of the code that are ignored by the compiler. Comments in the code are a primary source for system documentation. These are crucial for the work of software maintainers, as a basis for code traceability, for maintenance activities, but also for the use of the code itself as a library or framework in other projects. Although many software developers consider comments important, existing approaches to software quality analysis mainly disregard code comments and focus only on source code. This paper presents an approach, based on topic modeling, for analyzing the comments consistency to the source code. A model was provided to analyze the quality of comments in terms of consistency since comments should be consistent with the source code they refer to. The results show a similarity in the trend of topic distribution and it emerges that almost all classes are associated with no more than 3 topics.

Technical Debt (TD) concerns the lack of an adequate solution in a software project, from its design to the source code. Its admittance through comments or commit messages is referred to as Self-Admitted Technical Debt (SATD). Previous research has studied SATD from different perspectives, including its distribution, impact on software quality, and removal. In this paper, we investigate the relationship between refactorings and SATD removal. By leveraging a dataset of SATD and their removals in four open-source projects and by using an automated refactoring detection tool, we study the co-occurrence of refactorings and SATD removals. Results of the study indicate that refactorings are more likely to co-occur with SATD removals than with other commits, however, in most cases, they belong to different quality improvement activities performed at the same time.