This paper reviews the current role of Large Language Models (LLMs) in the treatment and support of people with autism spectrum disorder (ASD). We explore applications of LLM-based systems designed to improve communication, social skills, and emotional learning for individuals with ASD, highlighting their ability to generate personalized conversational interactions and simulate social scenarios. Current research demonstrates promising results in different domains, such as dialogue interventions, emotional recognition training, and work-related communication assistance. However, significant challenges remain, including ethical concerns about overreliance on AI, personalization, and privacy. This review synthesizes recent findings, highlights gaps in the existing literature, and proposes directions for future investigations. It particularly underscores the need for real-world applications and long-term studies to assess the efficacy of LLM efficacy in interventions for ASD.

Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI’s GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians’ ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings.

The increasing diffusion of mobile devices and their integration with sophisticated hardware and software components has promoted the development of numerous applications in which developers find new ingenious ways to exploit the possibilities offered by the access to resources such as cameras, biometric sensors, and GPS receivers. As a result, we are increasingly used to seeing applications that make extensive use of sensitive resources, potentially dangerous for our privacy. To address this problem, the latest approach to support user awareness in terms of privacy is represented by the Privacy Indicators (PI), a software solution implemented by the operating system to provide a visual stimulus to inform users whenever a dangerous resource is exploited by the app. However, the effectiveness of this approach has not been assessed yet. In this article, we present the result of a study on the effectiveness of using the PI to inform the user every time an app accesses the mobile device camera or microphone. We have chosen these two resources as the PI are currently implemented only for a very limited number of permissions. The controlled experiment involved 122 Android users who were asked to complete a series of tasks on their smartphone through prototypes using the involved resources in an explicit and latent way. Although the PI mechanism is very similar between Android and iOS, we have decided to focus on the former due to its greater diffusion. The results show no significant correlation between the use of PI and the detection of the resource being used by the app, suggesting that the effectiveness of PI in improving sensitive-related resources usage awareness, as currently implemented, is still unsatisfactory. In order to understand if the problem was due to the specific implementation of the PI, we implemented an enhanced version and compared it with the standard one. The results confirmed that an implementation that makes the indicators more visible and that is clearer in highlighting the fact that the app is accessing a resource improves resources usage awareness.

Data usually evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to model the data life cycle by observing the data evolution in its life cycle. Thus, we represent users able to alter data through timed automata. Through model checking, the obtained profile of evolution can be used to detect anomalies in relational database, data warehouse and big data.

Considering the increasing diffusion of mobile devices, attackers started to explore the possibility to perpetrate attacks using mobile surfaces (i.e., smartphones and tablets). Unfortunately, common antimalware techniques are often ineffective to detect new threats with the current signature based approach mainly adopted. In this we paper propose a set of features with the aim to discriminate between malware and trusted mobile applications: in detail we design a cascade learner where the first classifier of the cascade performs a coarse-grain analysis (it discriminates between malware and legitimate apps), while the second one performs a fine-grain analysis (it is aimed to identify the malware family). We obtain a precision equal to 0.947 and a recall equal to 0.962 in legitimate samples identification, while a precision equal to 0.961 and a recall equal to 0.946 is obtained in malware detection. With regard to family identification, an average precision and recall of 0.961 is obtained across 12 malware families.

Data tipically evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to detect data anomalies. We represent data user behaviours in terms of labelled transition systems and through the model checking techniques we demonstrate the proposed modeling can be exploited to successfully detect data anomalies.

The ineffectiveness of signature-based malware detection systems prevents the detection of malware, even objects of trivial obfuscation techniques, makes mobile devices vulnerable. In this paper a dynamic technique to detect malware on Android platform is proposed. We exploit a set of energy related features i.e., feature which can be symptomatic of abnormal battery consumption. We built different models exploiting four different supervised machine learning classification algorithms, obtaining for all the evaluated models an accuracy greater than 0.91.

Considering the widespread diffusion of mobile applications in many aspects of our life, one of the most required developer's skill is related to mobile applications development, with particular regard to the Android environment. In this paper, we present a framework aimed to quantify students learning ability in mobile applications development. The proposed framework can be useful also to teachers in order to improve their teaching method.

Code smells were defined as symptoms of poor design choices applied by programmers during the development of a software project [2]. They might hinder the comprehensibility and maintainability of software systems [5]. Similarly to some previous work [3, 4, 6, 7] in this paper we investigate the relationship between the presence of code smells and the software change- and fault-proneness. Specifically, while previous work shows a significant correlation between smells and code change/fault-proneness, the empirical evidence provided so far is still limited because of:

Code smells are symptoms of poor design and implementation choices that may hinder code comprehensibility and maintainability. Despite the effort devoted by the research community in studying code smells, the extent to which code smells in software systems affect software maintainability remains still unclear. In this paper we present a large scale empirical investigation on the diffuseness of code smells and their impact on code change- and fault-proneness. The study was conducted across a total of 395 releases of 30 open source projects and considering 17,350 manually validated instances of 13 different code smell kinds. The results show that smells characterized by long and/or complex code (e.g., Complex Class ) are highly diffused, and that smelly classes have a higher change- and fault-proneness than smell-free classes.

We present a practical approach for teaching two different courses of Software Engineering (SE) and Software Project Management (SPM) in an integrated way. The two courses are taught in the same semester, thus allowing to build mixed project teams composed of five-eight Bachelor's students (with development roles) and one or two Master's students (with management roles). The main goal of our approach is to simulate a real-life development scenario giving to the students the possibility to deal with issues arising from typical project situations, such as working in a team, organising the division of work, and coping with time pressure and strict deadlines.

Abstract We present ADvanced Artefact Management System (ADAMS), a web‐based system that integrates project management features, such as work‐breakdown structure definition, resource allocation, and schedule management as well as artefact management features, such as artefact versioning, traceability management, and artefact quality management. In this article we focus on the fine‐grained artefact management approach adopted in ADAMS, which is a valuable support to high‐level documentation and traceability management. In particular, the traceability layer in ADAMS is used to propagate events concerning changes to an artefact to the dependent artefacts, thus also increasing the context‐awareness in the project. We also present the results of experimenting with the system in software projects developed at the University of Salerno. Copyright © 2010 John Wiley & Sons, Ltd.

Rationale is the justification behind decisions taken during the software development process. The usefulness of rationale pervades the entire software lifecycle. However, it is during maintenance that the benefits of rationale management are most evident, as it provides an insight into the motivations and the reasoning behind decisions taken during the original design and implementation. One of the strongest limitation to the capturing of rationale information during development concerns its time-consuming and disruptive nature that cause many organizations to consider rationale management costs excessive. A possible solution is to extract and capture rationale information when it is needed. This can be done by analyzing documents shared or exchanged among software engineers during the development process. In this paper, we propose to supports the software engineer during the rationale capturing by automatically identifying candidate rationale information extracted from email repositories. Besides this, we also support the designer during the rationale retrieval by identifying possible rational information within a document repository starting from a query represented by a source document.

Concurrent versioning of source code is a common and well-established practice to manage concurrency and consistency within source code repository. Similarly to source code, software models are often the result of cooperative work by different software engineers, that need to update them even concurrently. Unfortunately, modeling tools rarely provide support for concurrency and consistency. On the other hand, the available concurrent versioning tools do not provide an adequate support for software models. In this paper we present COMOVER (COncurrent MOdel VERsioning), a tool that integrates software modeling features with versioning and concurrency management as well as model elements sharing and exchanging.

We propose a distributed inspection method that tries to minimise the synchronous collaboration among team members to identify defects in software artefacts. The approach consists of identifying conflicts on the potential defects and then resolving them using an asynchronous discussion before performing a traditional synchronous meeting. This approach has been implemented in a Web based tool and assessed through a controlled experiment with master students in Computer Science at the University of Salerno. The tool presented provides automatic merge and conflict highlighting functionalities to support the inspectors during the pre-meeting refinement phase and provides the moderator with information about the inspection progress as a decision support. The tool also supports a synchronous inspection meeting to discuss about unsolved conflicts. However, by analysing the data collected during a controlled experiment we found that this phase can often be skipped due to the fact that asynchronous discussion resolved most of the conflicts.

This research work investigates the use of a fine-grained management of software artefacts within a process support system named ADAMS (ADvanced artefact management system). In particular, configuration management of software artefacts and traceability management to support coordination and cooperation and of distributed teams during software evolution are addressed. ADAMS has been evaluated during the last two years within projects conducted by students of the software engineering courses of the computer science program at the University of Salerno (Italy) and evolved according to the user feedbacks.

The main drawback of existing software artifact management systems is the lack of automatic or semi-automatic traceability link generation and maintenance. We have improved an artifact management system with a traceability recovery tool based on Latent Semantic Indexing (LSI), an information retrieval technique. We have assessed LSI to identify strengths and limitations of using information retrieval techniques for traceability recovery and devised the need for an incremental approach. The method and the tool have been evaluated during the development of seventeen software projects involving about 150 students. We observed that although tools based on information retrieval provide a useful support for the identification of traceability links during software development, they are still far to support a complete semi-automatic recovery of all links. The results of our experience have also shown that such tools can help to identify quality problems in the textual description of traced artifacts.

Distributed software development is increasingly becoming a common practice in the software industry. The increased complexity of software systems also reflects in the complexity of design documentation, thus requiring a specific tool support for change and configuration management in distributed development settings. We present the fine-grained versioning management approach adopted in the ADAMS artefact management system, focusing on support to high level documentation versioning. We also present the results of experimenting the tool in software development projects developed at the University of Salerno

Applying information retrieval (IR) techniques to retrieve all correct links between software artefacts is in general impractical, as usually this means producing a high effort for discarding too many false positives. We show that the only way to recover traceability links using IR methods is to identify an "optimal" threshold that achieves an acceptable balance between traced links and false positives. Unfortunately, such threshold is not known a priori. For this reason we have devised the need to use an incremental traceability recovery approach to gradually identify the threshold where it is more convenient to stop the traceability recovery process, and provide evidence of this in a case study. We also report the experience of using the incremental traceability recovery during the development of software projects

We present a traceability recovery method and tool based on latent semantic indexing (LSI) in the context of an artefact management system. The tool highlights the candidate links not identified yet by the software engineer and the links identified but missed by the tool, probably due to inconsistencies in the usage of domain terms in the traced software artefacts. We also present a case study of using the traceability recovery tool on software artefacts belonging to different categories of documents, including requirement, design, and testing documents, as well as code components.