The Quantum Frontier of Software Engineering: A Systematic Mapping Study

Context. Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. Objective. This paper presents a systematic mapping study of the current state of QSE research, aiming to identify the most investigated topics, the types and number of studies, the main reported results, and the most studied quantum computing tools/frameworks. Additionally, the study aims to explore the research community's interest in QSE, how it has evolved, and any prior contributions to the discipline before its formal introduction through the Talavera Manifesto. Method. We searched for relevant articles in several databases and applied inclusion and exclusion criteria to select the most relevant studies. After evaluating the quality of the selected resources, we extracted relevant data from the primary studies and analyzed them. Results. We found that QSE research has primarily focused on software testing, with little attention given to other topics, such as software engineering management. The most commonly studied technology for techniques and tools is Qiskit, although, in most studies, either multiple or none specific technologies were employed. The researchers most interested in QSE are interconnected through direct collaborations, and several strong collaboration clusters have been identified. Most articles in QSE have been published in non-thematic venues, with a preference for conferences. Conclusions. The study's implications are providing a centralized source of information for researchers and practitioners in the field, facilitating knowledge transfer, and contributing to the advancement and growth of QSE

DevOps and Quality Management in Serverless Computing: The RADON Approach

The onset of microservices and serverless computer solutions has forced an ever-increasing demand for tools and techniques to establish and maintain the quality of infrastructure code, the blueprint that drives the operationalization of large-scale software systems. In the EU H2020 project RADON, we propose a machine-learning approach to elaborate and evolve Infrastructure-as-Code as part of a full-fledged industrial-strength DevOps pipeline. This paper illustrates RADON and shows our research roadmap

Data Mesh: a Systematic Gray Literature Review

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has picked the practitioners' interest, and there is considerable gray literature on it. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The review provides insights into practitioners' perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature

Calibración y validación de un modelo de crecimiento para alfalfa (Medicago sativa L.)

En el presente trabajo se modificó un modelo de crecimiento generado por McCall y Bishop-Hurley para pasturas compuestas de gramíneas templadas perennes (Modelo McCall). El objetivo fue desarrollar un modelo de crecimiento capaz de representar el crecimiento aéreo de pasturas de alfalfa (Modelo Alfalfa) sujetas a diferentes condiciones ambientales y de manejo de la defoliación. Se trabajó con pasturas puras de alfalfa sin reposo invernal en la región central de Argentina (localidades de Manfredi, Rafaela, Susana, Marcos Juárez y Paraná). En la etapa de calibración se realizaron modificaciones para representar el crecimiento de pasturas de alfalfa que crecieron sin limitantes hídricas y nutricionales y de pasturas sometidas a distintas frecuencias de defoliación. Se modificó: 1) la relación entre temperatura media diaria del aire y la eficiencia de uso de la radiación solar global para crecimiento aéreo (parámetro α); 2) la ecuación que considera la importancia de las reservas en raíz utilizadas por las plantas durante el rebrote; y 3) el parámetro α para simular pasturas sujetas a defoliaciones de distinta frecuencia. En la etapa de validación, se observó que el Modelo Alfalfa representó adecuadamente variaciones en crecimiento asociadas tanto a variaciones en la disponibilidad de agua como a variaciones en el manejo de la defoliación. Se concluye que el Modelo Alfalfa es capaz de representar los cambios en el crecimiento causado por variaciones en los principales factores bióticos (defoliación) y abióticos (clima) del ambiente.A model originally developed by McCall and Bishop-Hurley to predict the growth of temperate perennial grasses (Modelo McCall) was modified. The aim was to develop a model capable to describe the aboveground growth of alfalfa pastures (Modelo Alfalfa) subjected to several climate and defoliation conditions. We used winter-active alfalfa pastures growing at a central region of Argentina (cities of Manfredi, Rafaela, Susana, Marcos Juárez and Paraná). Modifications realized at calibration step were made to represent the growth of alfalfa pure stands growing under non limiting conditions (i.e. irrigated and fertilized pastures) and that of pastures subjected to different defoliation frequencies. We modified: 1) the relationship between mean air daily temperature and solar radiation use efficiency (parameter α); 2) the equation taking account the use root reserves during a regrowth; and 3) parameter α to simulate pastures subjected to contrasting defoliation frequencies. At the validation step, we observed that Modelo Alfalfa adequately describe changes in aerial growth associated to variations in both, water availability and defoliation management. It was concluded that the Modelo Alfalfa is capable of representing the variations in growth caused by variations of mains biotic (defoliation) and non-biotic (climate) environmental factors.EEA BalcarceFil: Berone, German Dario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Di Nucci, Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Fernández, H. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Gastaldi, Laura Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Mattera, Juan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Spada, María del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentin

The role of meta-learners in the adaptive selection of classifiers

The use of machine learning techniques able to classify source code components in defective or not received a lot of attention by the research community in the last decades. Previous studies indicated that no machine learning classifier is capable of providing the best accuracy in any context, highlighting interesting complementarity among them. For these reasons ensemble methods, that combines several classifier models, have been proposed. Among these, it was proposed ASCI (Adaptive Selection of Classifiers in bug predIction), an adaptive method able to dynamically select among a set of machine learning classifiers the one that better predicts the bug proneness of a class based on its characteristics. In summary, ASCI experiments each classifier on the training set and then use a meta-learner (e.g., Random Forest) to select the most suitable classifier to use for each test set instance. In this work, we conduct an empirical investigation on 21 open source software systems with the aim of analyzing the performance of several classifiers used as meta-learner in combination with ASCI. The results show that the selection of the meta-learner has not strong influence in the results achieved by ASCI in the context of within-project bug prediction. Indeed, the use of lightweight classifiers such as Naive Bayes or Logistic Regression is suggested