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.

Although Internet traffic detection and categorization have been extensively researched over the last decades, it remains a hot issue in the Internet of Things (IoT) context, mainly when traffic is generated in medical structures. Theoretically, it is possible to apply classical methods for IoT traffic categorization and to detect traffic addressed to intelligent devices present in hospital rooms. The problem is always to get a proper medical IoT traffic dataset. In this work, we have created a synthetic dataset of IoT traffic generated by different smart devices put in different hospital rooms. For creating the medical IoT traffic, we have exploited IoT-Flock, an open-source tool for IoT traffic generation supporting CoAP and MQTT, the most used IoT protocols. We have performed, for the first time, a multinomial classification of IoT-Flock-generated traffic considering both normal-traffic and packets of different attacks. The classification has been performed by comparing both traditional machine learning techniques and deep learning network models composed of several hidden layers. The obtained results are very encouraging and can confirm the usability of IoT-Flock data to be used to test and train machine and deep learning models to detect abnormal IoT traffic in a medical scenario.

The architectural space impacts the emotional state of its inhabitants. Nevertheless, no studies have investigated, to date, how it influences the perception of others' affective states, possibly impacting our social behavior. This paper analyzes the eye-gaze data collected during a social scenario recreated after a promenade within virtual architectures. Immersive and dynamic virtual architectures were characterized by decreasing or increasing sidewall distance, ceiling height, windows height, and different colors. At the end of such an experience, participants judged the arousal level expressed by a virtual avatar. For the first time, we apply machine and deep learning techniques to the behavioral, environmental, and eye-gaze features extracted during the dynamic experience of virtual architectures. In order to verify the feasibility of automated classification of the final arousal judgment on the avatar emotional expression, we have considered both interpretable, i.e., decision trees, and black-box models, i.e., dense neural networks. The decision tree reached an accuracy rate of 66%, showing the importance of eye-gaze parameters to classify the participants' arousal judgment. The black-box dense neural network increased the accuracy up to 80%. Overall, our findings demonstrate the capability of artificial intelligence methodologies to classify and possibly predict the arousal judgment of body expressions at the end of a virtual promenade. Such knowledge will serve the design and evaluation of future spaces by combining virtual reality and artificial intelligence within the experience of architecture. In such a way, it will be possible to predict the influence of the surrounding architecture on human social behavior.

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.

Heart disease is becoming the biggest cause of mortality worldwide. Its early detection can considerably lower the risk of mortality and help to promote its successful treatment. However, this early detection necessitates regular monitoring of a wide range of clinical and lifestyle factors. This is why a growing number of studies are being conducted to automate the forecasting of cardiac diseases, beginning with an examination of ECG images, which is the first diagnostic test performed on patients and also the most simple and economical to conduct. This study investigates the use of three groups of ECG images acquired from three separate sets of cardiac patients, with different heart-related illnesses, and a set of healthy controls to predict heart disease using deep learning classifiers. The evaluation is carried out on a real-life dataset, and the results highlight really interesting findings.

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.

The detection and classification of Internet traffic was studied in depth in the last twenty years, but this is still an open research issue as pertains the Internet of Things (IoT), mainly because real IoT traffic dataset are not very widespread. With this paper, we make public an integrated dataset, made of actual IoT network flows, built using six different network sources, which could represent a research reference for further investigations. Furthermore, we exploited it to optimize the hyper-parameters of a deep neural network and evaluate its performance for both distinguishing normal and abnormal traffic and discriminating different types of attacks, achieving very good results.

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.

Identifying and diagnosing as early as possible malignant lung nodules is essential to reduce the mortality of lung cancer patients. Radiologists employ computer tomography scan to detect cancer in the body and track its growth. Interpretation of tomography scan, today still not automated, can lead to cancer detection at early stages, thus leading to the treatment of cancer which can decrease the death rates. Image processing, a branch of computer-assisted diagnostic, can support radiologists for the early detection of cancer. Against that background, we propose a novel ensemble-based approach for more accurate lung cancer classification using Computer tomography scan images. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet), combined into an ensemble architecture to classify clustered images of lung lobes. The approach is validated on a real dataset and shows that the ensemble classifier ensures effective performance, exhibiting better generalization capabilities.

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%.

In this paper, we present a Deep Learning architecture, exploiting a fuzzy layer, applied to the data coming from various sensors located under the feet of a patient affected by the Parkinson's disease. The solution we propose permits one to cluster data coming from different sensors into different fuzzy partitions, according to the different parts of the feet, and to discriminate the illness of a person as well as the severity degree of the disease itself. We employed a known dataset to evaluate our solution and compared its performance with some similar approaches found in the relevant literature. Moreover, we performed an intensive parameter optimization step to find the best setting for the proposed fuzzy neural network. The evaluation shows that our solution obtains good classification results both in the binary and in the multiclassification approach.

Mobile devices have become, in the last years, an essential tool used to perform daily activities. However, they also have become the target of continuous malware attacks usually coming out from new malware obtained as a variant of existing ones. For this reason, we suppose that by comparing the behavior of a new application with those of known malware applications it is possible to define it as malicious or trusted. According to this, the current study proposes an approach based on a data-aware declarative process mining technique to identify similarities and recurring patterns in the system call traces generated by a set of malicious mobile applications. The obtained characterization, represented by a set of declarative constraints within their data attributes, can be considered as a run-time fingerprint of a malware useful to evaluate the membership of a new application to a given malware family. The empirical validation of the proposed approach is performed on a dataset of more than 1200 trusted and malicious applications coming out from eight malware families and the obtained results show a very good discrimination ability.

Parkinson's disease is a degenerative movement disorder causing considerable disability. However, the early detection of this syndrome and of its progression rates may be decisive for the identification of appropriate therapies. For this reason, the adoption of Neural Networks to detect this disease on the base of walking information is gaining more and more interest. In this paper, we defined a Deep Neural Network based approach allowing one to exploit the information coming from various sensors located under the feet of a person. The proposed approach allows one to discriminate people affected by the Parkinson syndrome and detect the progression rates of the disease itself. To evaluate the proposed architecture we used a known dataset with the aim to compare its performance with other similar approaches. Moreover, we performed an in-depth hyper-parameter optimization to find out the best neural network configuration for the specific task. The comparison shows that the proposed classifier, trained with the best parameters, outperforms the results proviously obtained in other studies on the same dataset.

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.

Code reuse by copying a code fragment with or without modification generates duplicate copies of exact or similar code fragments in a software system, known as code clones. The debate about the harmfulness of clone in ongoing in the literature, nevertheless, it is widely recognized that clones needs special considerations during software evolution. In this paper, it is proposed a quantitative analysis of technical debt values to understand if it is higher with cloned code than those without cloned code. Moreover, changes performed on these files have been analyzed by analyzing commit logs. According to our inspection on four subject systems, the technical debt of files with cloned code is significantly higher than those without cloned code. Moreover, as expected, files with cloned code are more impacted by changes.

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.

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.

A relevant aspect of development and maintenance tasks is the evaluation of the software system quality.Measurement tools facilitate the measurement of software metrics and application of the quality models.However, differences and commonalities exist among the evaluation results obtained by the adoption of different measurement tools.This does not allow an objective and unambiguous evaluation of a software product quality level.In this direction, this paper proposes a preliminary investigation on the impact of measurement tools on the evaluation of the software maintainability metrics.Specifically, metrics values have been computed by using different software analysis tools for three software systems of different size.Measurements show that the considered measurement tools provide different values of metrics evaluated for the same software system.

Software engineers have successfully used Natural Language Processing for refactoring source code. Conversely, in this paper we investigate the possibility to apply software refactoring techniques to textual content. As a procedural program is composed of functions calling each other, a document can be modeled as content fragments connected each other through links. Inspired by software engineering refactoring strategies, we propose an approach for refactoring wiki content. The approach has been applied to the EMF category of Eclipsepedia with encouraging results.

The relationships existing between a business process and the supporting software system is a critical concern for the organizations, as it directly affects their performance. The research described in this paper is concerned with the use of information retrieval techniques to software maintenance and, in particular, to the problem of recovering traceability links between the business process models and the components of the supporting software system.

The presence of vulnerable statements in the source code is a crucial problem for maintainers: properly monitoring and, if necessary, removing them is highly desirable to ensure high security and reliability. To this aim, a number of static analysis tools have been developed to detect the presence of instructions that can be subject to vulnerability attacks, ranging from buffer overflow exploitations to command injection and cross-site scripting.Based on the availability of existing tools and of data extracted from software repositories, this paper reports an empirical study on the evolution of vulnerable statements detected in three software systems with different static analysis tools. Specifically, the study investigates on vulnerability evolution trends and on the decay time exhibited by different kinds of vulnerabilities.

Design patterns are solutions to recurring design problems, conceived to increase benefits in terms of reuse, code quality and, above all, maintainability and resilence to changes. This paper presents results from an empirical study aimed at understanding the evolution of design patterns in three open source systems, namely JHotDraw, ArgoUML, and Eclipse-JDT. Specifically, the study analyzes how frequently patterns are modified, to what changes they undergo and what classes co-change with the patterns. Results show how patterns more suited to support the application purpose tend to change more frequently, and that different kind of changes have a different impact on co-changed classes and a different capability of making the system resilent to changes.

The Web services stack of standards is designed to support the reuse and the interoperation of software components on the Web. A critical step in the process of developing applications based on the service oriented architecture is the service discovery. This paper shows how service composition can be used as a technique to support service discovery. The paper discusses the current state of research in this area and introduces a semantic matching algorithm that exploits the possibility to compose multiple services in order to satisfy a service request.

Abstract The enactment of e Business processes requires the effective usage of the existing legacy applications in the e Business initiatives. Technical issues are not enough to drive the evolution of the existing legacy applications, but problems concerning the perspectives, strategies, and business of the enterprises have to be considered. In particular, there is a strict relationship between the evolution of the legacy systems and the evolution of the e Business processes. This paper proposes a strategy to extract the requirements for a legacy system evolution from the requirements of the e Business evolution. The proposed strategy aims at characterizing the software system within the whole environment in which its evolution will be performed. It provides a useful set of attributes addressing technical, process, and organizational issues. Moreover, a set of assessment activities is proposed affecting the order in which the attributes are assessed. Copyright © 2004 John Wiley & Sons, Ltd.

Workflow management systems have proven useful for improving the management end execution of processes in several application domains, including software engineering. In this paper we discuss issues and preliminary results of a project aiming at introducing workflow technology in a software maintenance organization. We apply a four steps process to model the workflows, and the associated flows of documentations of the massive maintenance process. The paper describes the models obtained and discusses how they have been implemented using market-widespread workflow technology.

The use of workflow technology to provide automated support to the management and execution of software engineering processes has become of great interest for large software companies that nowadays are moving towards the new model of "virtual" organizations. Therefore, workflow systems that allow co-operation among team members in a distributed environment are now a primary concern. In this paper we present a new workflow management system, named FlowManager, which has all the potential to be used in such application domain. FlowManager, in fact, will be a component of a larger European GENESIS project (Generalized Environment for Process Management in Cooperative Software Engineering), whose objective is to develop a non-invasive and open source environment for modeling software engineering processes and managing co-operation among geographically distributed teams.

Visual query languages represent an evolution, in terms of understandability and adaptability, with respect to traditional textual languages. We present an iconic query system that enables the interaction of a novice user with a relational database. Our goal is to help a novice user to learn and comprehend the relational data model and a textual query language such as SQL, through the use of the iconic metaphore. In this sense our approach is different from most of the visual query systems proposed in the literature that present the user with a higher level query language, hiding the underlying data model. We also present results from an experiment conducted with first year students to evaluate the effectiveness of our approach.

The need for automatic support of business processes that extend over the boundaries of an enterprise is a recognized need of emerging virtual organizations. To make workflow technologies useful during the enactment of business processes involving many partners that reciprocally provide and consume services, it is important to provide a model, and supporting technologies, to manage the introduction of services in workflow models.This paper introduces a framework for the introduction of eServices in business process models. The framework comprises RDF based languages to model processes, services, and service composition, and supporting technologies to generate executable workflow models, including interfaces to the actual services, from the models.

Abstract This case study presents the results from a pilot project aimed at introducing workflow management technologies and a Web‐based software tool in a large software enterprise. In particular, we analyzed and modeled the workflows and documents at the site of the ordinary maintenance process and implemented a prototype for the management of the process using a commercial‐Web‐based workflow management system. This paper reports on the experience gained from a 10‐month project, which included the experimental use at a single site of the workflow prototype for 4 months in an industrial setting involving more than 800 maintenance service requests on a large software system. Copyright © 2002 John Wiley & Sons, Ltd.

Software organizations are moving from traditional software factory models towards virtual organization models, where distributed teams converge in a temporary network with the aim of integrating different competences or solving problems in a cooperative way. Most workflow management systems of last generation are web based and this makes them a viable enabling technology for remodeling both the organization structure and its processes in order to move towards a virtual organization model and increase its competitiveness. We present a case study of introducing workflow technologies in a large software enterprise. In particular, a workflow-based prototype implementation for the management of the ordinary maintenance process is discussed.