In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top level. Within each crop cluster, clients collaboratively train specialized models tailored to specific crop types, which are then aggregated to produce a higher-level global model that integrates knowledge across multiple crops. This hierarchical design enables both local specialization for individual crop types and global generalization across diverse agricultural contexts while preserving data privacy and reducing communication overhead. Experiments demonstrate the effectiveness of the proposed system, showing that local and crop-layer models closely follow actual yield patterns with consistent alignment, significantly outperforming standard machine learning models. The results validate the advantages of hierarchical federated learning in the agricultural context, particularly for scenarios involving heterogeneous farming environments and privacy-sensitive agricultural data.

Context: Estimating effort has always been considered an important element at the start of each software development project. The challenge of estimating the effort of software development lies in its precision. With the emergence of agile methodologies, methods for effort estimation (EE) had to adapt to this new development path. In this article, we are conducting a systematic mapping study on effort estimation in the context of agile software development. Objective: we want to identify the estimation approaches and techniques used in the context of agile development to better understand the specifics and trends relating to this mode of development. Method: we conducted a systematic mapping study by adopting the guideline explained in[1] [2]. A systematic review of the literature [3] has already been carried out for publications between 2001 and 2013. This work is an extension of this previous study. We queried 5 electronic databases. Conclusion: We retrieved 11350 paper from five electronic databases. A total of 108 papers is selected after applying the inclusion and exclusion criteria. Based on the results, there is a general increase over the years of studies concerning effort estimation in agile software development.

Software effort estimation is a key factor for software development project success. Case-based reasoning (CBR), as a viable alternative to analogy methods, has often been used as a reference to calculate effort estimation. Albeit its conceptual simplicity, however, CBR-based effort estimation seems difficult and complex in reality, especially when enhancement on the estimate precision is highly desirable. For an effective and fast estimation, we present in this paper an innovative tool, automated-SEE (software effort estimation), for estimating software development effort using improved CBR. This tool enables the consideration of a comprehensive set of different types of requirements, including both functional requirements (FRs) and non-functional requirements (NFRs), and domain properties (DPs). Application of this tool to 36 (students') projects shows that the resulting software effort estimation is highly reliable.

The aim of this paper is to empirically evaluate a framework that we have developed on the use of the software quality standard ISO 9126 with the Differentiated Services (DiffServ) Quality of Service (QoS) model to assess the software quality of mobile applications, especially the software usability characteristic. To do that, firstly we have carried out an empirical evaluation of the influence of mobile limitations on Diffserv classes by means of the Sensorly tool. Thereafter, a correlation between the influence of mobile limitations on DiffServ classes and its effects on usability characteristic is discussed. As a result of this empirical study, the usability is less correlated with Diffserv classes in terms of influence of mobile limitations given that this characteristic corresponds to the presentation layer unlike the Diffserv model which would focus on the network level quality.