Services for data provisioning can be extremely valuable. Organizations wrestle with deciding whether to “expose or not to expose” their data through Web APIs and, depending on the nature of their business, mull over potential benefits and drawbacks. This is because data can be exploited and misused in unanticipated ways. Rate restriction techniques are frequently used to manage data access and protect back-end computing resources. The best choice for the maximum rate at which information can be “safely” provided to clients will determine its effectiveness. An important situation involves formal service-level agreements that govern the quality of services provided by public governments and private businesses. These firms might need to decide on a rate cap for their Web APIs that prevents unauthorized clients from accurately calculating service levels while still enabling the development of valuable value-added services. In this paper, we propose a statistical model for this problem and a technique, based on sampling tools, following brute-force, binary search, and heuristic search methods, to select an appropriate rate limit, and demonstrate its validity through a case study involving a large Italian bus company.

Balancing the utilization of model resources with the accuracy of the prediction is a significant challenge in energy consumption analysis. It is then essential to select models that combine computational efficiency with suitable predictive accuracy to support sustainable environments. We propose an approach to evaluate the sustainability level of popular machine learning (ML) and deep learning (DL) models, offering decision-makers the flexibility to select the most appropriate model, considering resource efficiency and prediction reliability. It is demonstrated using a novel dataset comprising detailed energy consumption data from four major social mass-housing buildings in Rome. It is collected over two years and is enriched with energy bills, resident surveys, and daily usage patterns.

ABSTRACT The demand for efficient functional size measurement (FSM) methods in the competitive software market today is undeniable. However, incomplete and imprecise system specifications pose significant challenges, particularly in scenarios that require fast, flexible, and accurate software size estimation, such as public tenders. Although the integration of conceptual models within FSMs offers a promising solution to these issues, a systematic exploration of such methods remains largely unexplored. This work evaluates FSM methods that integrate conceptual models by analyzing studies from the past 20 years. It highlights key contributions and advances in proposed conceptual model‐based FSM methods. In addition, the study examines their limitations and challenges, offering insights for future improvements. A systematic literature review (SLR) was conducted to guide the research process. The review was organized around three research questions, each targeting the study's key objectives: (1) to explore FSM methods utilizing conceptual models, (2) to summarize proposals for their improvement, and (3) to identify the limitations of the proposed enhancements. Primary studies span two decades (2004–2024), with peaks in 2008 and 2015, averaging one to two studies annually. Of the 1371 initial studies, 13 were selected using strict criteria. These studies are categorized into Measurement Techniques (30.77%), Automation (38.46%), and Application‐Specific topics (30.77%). The contributions of the primary studies are analyzed in terms of their approaches Repeatability and Validation. Repeatability is assessed by examining whether the primary studies proposed a formal model when using real datasets. In contrast, Validation focuses on whether the studies were tested in real‐world projects. A total of 46.15% of the primary studies utilize formal models, whereas 53.85% rely on nonformal models, although dataset size is often unspecified. Most studies validate their methods using 1 to 30 projects. Common Software Measurement International Consortium (COSMIC) is the most widely used FSM method (69.23%), followed by the Function Point Analysis (FPA) (15.38%) and custom Methods (15.38%), with conceptual UML models appearing in 84.61% of the studies. Key limitations, including Scalability and Generalizability, Complexity Robustness, and Flexibility, persist across all categories. Notably, Scalability and Generalizability was identified as a limitation in 75% of Measurement Techniques studies, 80% of Automation studies, and 75% of Application‐Specific studies, while Flexibility challenges were most pronounced, affecting 100% of Application‐Specific studies. The limited number of primary studies underscores a substantial research gap in conceptual model‐based FSM methods. Future research should focus on developing formal models to enhance theoretical rigor, leveraging real‐world datasets for validation, providing comprehensive methodological descriptions, and standardizing validation practices. Additionally, prioritizing advancements in FSM methods by improving scalability, generalizability, and flexibility is crucial. These enhancements will enable FSM methods to effectively manage complex systems, adapt across diverse software domains, and address application‐specific requirements, ensuring their continued relevance in dynamic and evolving software development environments.

Effort estimation poses a significant challenge in software development as it encompasses the determination of the necessary time and resources for the completion of a project. Several approaches have been proposed to estimate the effort in software development, but they often exhibit various limitations which can be grouped into three main categories. Firstly, these methods heavily rely on expert judgment, introducing subjectivity and variations in estimation. Secondly, they can be complex to comprehend and implement, requiring a deep understanding of software metrics and project attributes. Finally, they usually need manual effort in collecting and analyzing data, leading to potentially time-consuming tasks and the possibility of errors. This paper presents a solution that addresses these challenges by utilizing established conceptual models like Business Process Management Notation (BPMN) to develop a comprehensive system description through the extraction of entities and their relationships as semantic triples. This approach lays the foundation for identifying conceptual micro-services, enabling a precise breakdown of system functionalities, and incorporates prompt engineering with ChatGPT for suitable effort estimation of each micro-service.

Clouds provide an illusion of an infinite amount of resources and enable elastic services and applications that are capable to scale up and down (grow and shrink by requesting and releasing resources) in response to changes in its environment, workload, and Quality of Service (QoS) requirements. Elasticity allows to achieve required QoS at a minimal cost in a Cloud environment with its pay-as-you-go pricing model. In this paper, we present our experience in designing a feedback elastically controller for a key-value store. The goal of our research is to investigate the feasibility of the control theoretic approach to the automation of elasticity of Cloud-based key-value stores. We describe design steps necessary to build a feedback controller for a real system, namely Voldemort, which we use as a case study in this work. The design steps include defining touchpoints (sensors and actuators), system identification, and controller design. We have designed, developed, and implemented a prototype of the feedback elasticity controller for Voldemort. Our initial evaluation results show the feasibility of using feedback control to automate elasticity of distributed key-value stores.