Abstract
Contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. Previous research has demonstrated the value of contracts in traditional software development. However, the adoption and impact of contracts in the context of mobile app development, particularly of Android apps, remain unexplored. To address this, we present the first large-scale empirical study on the use of contracts in Android apps, written in Java or Kotlin. We consider contract elements divided into five categories: conditional runtime exceptions, APIs, annotations, assertions, and other. We analyzed 2,390 Android apps from the F-Droid repository and processed 52,977 KLOC to determine 1) how and to what extent contracts are used, 2) which language features are used to denote contracts, 3) how contract usage evolves from the first to the last version, and 4) whether contracts are used safely in the context of program evolution and inheritance. Our findings include: 1) although most apps do not specify contracts, annotation-based approaches are the most popular; 2) apps that use contracts continue to use them in later versions, but the number of methods increases at a higher rate than the number of contracts; and 3) there are potentially unsafe specification changes when apps evolve and in subtyping relationships, which indicates a lack of specification stability. Finally, we present a qualitative study that gathers challenges faced by practitioners when using contracts and that validates our recommendations.

Abstract
As different eBPF libraries keep emerging, developers are left with the hard task of choosing the right one. Until now, this choice has been based on functional requirements (e.g., programming language support, development workflow), while quantitative metrics have been left out of the equation. In this paper, we argue that efficiency metrics such as performance, resource usage, and data collection fidelity also need to be considered for making an informed decision. We show it through an experimental study comparing five popular libraries: bpftrace, BCC, libbpf, ebpf-go, and Aya. For each, we implement three representative eBPF-based tools and evaluate them under different storage I/O workloads. Our results show that each library has its own strengths and weaknesses, as their specific features lead to distinct trade-offs across the selected efficiency metrics. These results further motivate experimental studies to increase the community's understanding of the eBPF ecosystem.

Abstract
The concept of data spaces has emerged as a structured, scalable solution to streamline and harmonize data sharing across established ecosystems. Simultaneously, the rise of AI services enhances the extraction of predictive insights, operational efficiency, and decision-making. Despite the potential of combining these two advancements, integration remains challenging: data spaces technology is still developing, and AI services require further refinement in areas like ML workflow orchestration and energy-efficient ML algorithms. In this paper, we introduce an integrated architectural framework, developed under the Green.Dat.AI project, that unifies the strengths of data spaces and AI to enable efficient, collaborative data sharing across sectors. A practical application is illustrated through a smart farming use case, showcasing how AI services within a data space can advance sustainable agricultural innovation. Integrating data spaces with AI services thus maximizes the value of decentralized data while enhancing efficiency through a powerful combination of data and AI capabilities.

Abstract
Alzheimer's disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them.

Abstract

Abstract
[No abstract available]