Context: Maintaining alignment between software architecture and organizational goals is vital for efficiency and cost control during refactoring. Manual reassignment of many fine-grained code entities to target modules is labour-intensive when only heterogeneous textual documentation is available. Objectives: Provide an operational assessment of an embedding-based approach for mapping source code snippets to architectural modules and identify, in this setting, the textual data properties that most clearly condition classification performance. Method: A classification pipeline is applied to six diverse software systems. We classify both functions and whole files under multiple documentation granularities and relate macro F1 variability to four primary properties: class separability/density, lexical (vocabulary) overlap, class balance, and similarity of data distributions between training and test sets. Results: Performance varies markedly across systems and datasets; file-level entities generally outperform functions. In our experiments, classification gains align most consistently with higher class separability/density in the embedding space, while lexical overlap and class balance show weaker, granularity-dependent effects. Added textual granularity can aid function-level mapping when it improves separability and overlap, but may introduce noise for files. Conclusions: In the evaluated pipeline, embedding-based mapping most effectively streamlines early refactoring when documentation yields sufficient lexical overlap and separable, dense class regions. Practitioners using similar embedding-based approaches should estimate overlap and separability early; if both are low, our results suggest preferring file-level granularity or enriching descriptions before pursuing fine-grained (function) mapping.

The landscape of modern computing systems is shifting towards architectures built by combining available services under the “everything as a service” paradigm. These architectures are deployed on distributed cloud-edge infrastructures, aiming to provide innovative services to a wide range of users. However, it is crucial for these systems to address environmental sustainability concerns. This poses challenges in operating such systems in open, dynamic, and uncertain environments while minimizing their energy consumption. To tackle these challenges, we propose a decentralized service assembly approach that ensures the assembly is energetically self-sustainable by relying on locally harvested and stored energy. In our contribution, we introduce a general service selection template that enables the derivation of different selection policies. These policies guide the construction and maintenance of the service assembly. To evaluate their effectiveness in meeting the sustainability requirements, we conduct a comprehensive set of simulation experiments, providing valuable insights.

As simulation applications become vital for understanding and predicting complex systems, analyzing data from repeated simulation runs is essential to gauge model uncertainty and identify optimal parameter settings. This paper presents a visualization tool for analyzing time series ensemble data generated by discrete-event simulations, focusing on feature importance within clustering results. The tool combines dimensionality reduction, clustering, and SHapley Additive exPlanations (SHAP) to highlight influential features and identify trends within clustered simulation data, advancing previous approaches focusing solely on visualization or clustering without analyzing specific feature contributions. By analyzing a manufacturing use case, we show how the visualization supports decision-makers by depicting the main features driving cluster formation and displaying time intervals critical to characterizing distinct system behaviors.

Robots are increasingly adopted in a wide range of unstructured and uncertain environments, where they are expected to keep quality properties such as efficiency, accuracy, and safety. To this end, robots need to be smart and continuously update their situation awareness. Self-adaptive systems pave the way for accomplishing this aim by enabling a robot to understand its surroundings and adapt to various scenarios in a systematic manner. However, some situations, e.g., adjusting adaptation rules, refining run-time models, narrowing a vast adaptation domain, and taking future scenarios into consideration, etc. may require the self-adaptive system to include additional specialized components. In this regard, this work proposes a novel approach combining the MAPE-K, adaptive controllers, and a Digital Twin of the robot to enable the managing system to be aware of new scenarios appearing at run-time and operate safely, accurately, and efficiently. A state-of-the-art robot model is employed to evaluate the suitability of the approach.

Summary Modern software systems are required to dynamically adapt to changing workloads, scenarios, and objectives and to achieve a certain Quality of Service (QoS). Guaranteeing QoS requirements is not trivial, as run‐time uncertainty might invalidate the design‐time rationale, where software components have been selected by means of off‐line analysis. In this work, we propose a QoS‐based feedback approach that makes a combined use of design‐time predictions and run‐time measurements to manage QoS data over time and support software architects while selecting software components that best fit QoS requirements. We illustrate the feasibility and efficacy of the approach on a case study, where the quantitative evaluation shows how the analysis effectively identifies the sources of QoS violations and indicates possible solutions to achieve QoS requirements.

Modern applications, e.g., for pervasive computing scenarios, are increasingly reliant on systems built from multiple distributed components, which must be suitably composed to meet some specified functional and non-functional requirements. A key challenge is how to efficiently and effectively manage such complex systems. The use of self-management capabilities has been suggested as a possible way to address this challenge. To cope with the scalability and robustness issues of large distributed systems, self-management should ideally be architected in a decentralized way, where the overall system behavior emerges from local decisions and interactions. Within this context, we propose GOPRIME, a fully decentralized middleware solution for the adaptive self-assembly of distributed services. The GOPRIME goal is to build and maintain an assembly of services that, besides functional requirements, fulfils also global quality-of-service and structural requirements. The key aspect of GOPRIME is the use of a gossip protocol to achieve decentralized information dissemination and decision making. To show the validity of our approach, we present results from the experimentation of a prototype implementation of GOPRIME in a mobile health application, and an extensive set of simulation experiments that assess the effectiveness of GOPRIME in terms of scalability, robustness and convergence speed.

The computing and networking capacities of today's wireless portable devices allow for ubiquitous services, which are seamlessly networked. Indeed, wireless handheld devices now embed the necessary resources to act as both service clients and providers. However, the ubiquitous networking of services remains challenged by the inherent mobility and resource constraints of the devices, which make services a priori highly volatile. This paper discusses the design, implementation, and experimentation of the ubiSOAP service-oriented middleware, which leverages wireless networking capacities to effectively enable the ubiquitous networking of services. ubiSOAP specifically defines a layered communication middleware that underlies standard SOAP-based middleware, hence supporting legacy Web Services while exploiting nowadays ubiquitous connectivity.

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In this paper we present a compositional reasoning to verify middleware-based software architecture descriptions. We consider a nowadays typical software system development, namely the development of a software application A on a middleware M. Our goal is to efficiently integrate verification techniques, like model checking, in the software life cycle in order to improve the overall software quality. The approach exploits the structure imposed on the system by the software architecture in order to develop an assume-guarantee methodology to reduce properties verification from global to local. We apply the methodology on a non-trivial case study namely the development of a Gnutella system on top of the SIENA event-notification middleware.

This paper presents the design and evaluation of a support service for mobile, wireless clients of a distributed publish/subscribe system. A distributed publish/subscribe system is a networked communication infrastructure where messages are published by senders and then delivered to the receivers whose subscriptions match the messages. Communication therefore does not involve the use of explicit addresses, but rather emerges from the dynamic arrangement of publishers and subscribers. Such a communication mechanism is an ideal platform for a variety of Internet applications, including multiparty messaging, personal information management, information sharing, online news distribution, service discovery, and electronic auctions. Our goal is to support such applications on mobile, wireless host devices in such a way that the applications can, if they chose, be oblivious to the mobility and intermittent connectivity of their hosts as they move from one publish/subscribe access point to another. In this paper, we describe a generic, value-added service that can be used in conjunction with publish/subscribe systems to achieve these goals. We detail the implementation of the service and present the results of our evaluation of the service in both wireline and emulated wireless environments.