Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release Cycles

Release planning—deciding what features to implement in upcoming releases of a software system—is a critical activity in iterative software development. Many release planning methods exist, but most ignore the inevitable uncertainty in estimating software development effort and business value. The article’s objective is to study whether analyzing uncertainty during release planning generates better release plans than if uncertainty is ignored. To study this question, we have developed a novel release planning method under uncertainty, called BEARS, that models uncertainty using Bayesian probability distributions and recommends release plans that maximize expected net present value and expected punctuality. We then compare release plans recommended by BEARS to those recommended by methods that ignore uncertainty on 32 release planning problems. The experiment shows that BEARS recommends release plans with higher expected net present value and expected punctuality than methods that ignore uncertainty, thereby indicating the harmful effects of ignoring uncertainty during release planning. These results highlight the importance of eliciting and analyzing uncertainty in software effort and value estimations and call for increased research in these areas.

Guidelines for model adaptation: a study of the transferability of a general seagrass ecosystem DBN model

Ecological models are extensively and increasingly used in support of environmental policy and decision making. Dynamic Bayesian Networks (DBN) as a tool for conservation have been demonstrated to be a valuable tool for providing a systematic and intuitive approach to integrating data and other critical information to help guide the decision-making process. However, data for a new ecosystem are often sparse. In this case, a general DBN developed for similar ecosystems could be applicable, but this may require the adaptation of key elements of the network. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. We adapted a general DBN of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Z. marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.

Vaccination of elite athletes against SARS-CoV-2 and broader implications for public health policy

SARS-CoV-2, a human β-coronavirus implicated as thecausative agent in the COVID-19 pandemic, has been the subject of the most globally intensive vaccine development effort inrecorded history. The spectrum of SARS-CoV-2 vaccine candidates, deployedglobally, demonstrates an expansive diversity in regardsto design philosophies and immunological mechanisms of action. In the context of an aging, physically deconditioned, and overweight global population, which finds itself heavily burdened by a high prevalence of non-communicable chronic disease; elite strength, power and endurance athletes represent a minority population comprised of extreme physiological outliers. This report explores the molecular toxicity and pathophysiology of the SARS-CoV-2 spike protein, the design and immunological strategies embodied by the spectrum of SARS-CoV-2 vaccine candidates, and the intersection of these phenomena with the demographic, lifestyle and physiological characteristics of elite athletes; so as to inform vaccination strategies against SARS-CoV-2 which most protect this outlying minority population.

Analysis and Comparison of Neural Network Models for Software Development Effort Estimation

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.

Progress on neutronic analysis for CFETR

The neutron induced irradiation field is a key problem in fusion reactor related to nuclear responses, shielding design, nuclear safety, and thermo-hydraulic analysis. To support the system design of China Fusion Engineering Test Reactor (CFETR), the comprehensive analysis of irradiation field has been conducted in support of many new developed advanced tools. The paper first summarizes the recent progress on related neutronics code development effort including the geometry conversion tool cosVMPT, Monte Carlo variance reduction technology ‘on-the-fly’ global variance reduction (GVR). Such developed tools have been fully validated and applied on the CFETR nuclear analysis. The neutron irradiation has been evaluated on CFETR Water Cooled Ceramic Breeder (WCCB) blanket, divertor, vacuum vessel, superconductive coils and four kinds of heating systems including the Electron Cyclotron Resonance Heating (ECRH), Ion Cyclotron Resonance Heating (ICRH), Low Hybrid Wave (LHW) and Neutral Beam Injection (NBI). The nuclear responses of tritium breeding ratio (TBR), heating, irradiation damage, Hydrogen/Helium (H/He) production rate of material have been analyzed. In case of neutron damage and overheating deposition on the superconductive coils and Vacuum Vessel (VV), the interface and shielding design among heating systems, blanket and other systems has been initialized. The results show the shielding design can meet the requirement of coil and VV after several iterated neutronics calculation.

Development of Digital Libraries with Software Product Line Engineering

Digital Libraries have become popular nowadays since important libraries all over the world started distributing their collections online, properly classified, and, in many cases, with access to the digital version of the resource. These programs have been beneficial to the general population as well as research groups in fields such as language and literature. Nonetheless, since their creation is a time-consuming and costly process, small organizations are forced to rely on obsolete or poorly designed software. However, most of the features, including the data model, are shared by this type of system, with minor variations depending on the type of resources to be handled. This article presents a Software Product Line (SPL) for the semi-automatic generation of Digital Libraries (DL). Our SPL allows developers to specify which DL features are required, which will define the data model variationand the generated source code. The specification is then transformed into a fully functional DL application with the specified features that is ready for deployment. We present the feature model, the SPL implementation, and acase study on three sample projects that enabled us to evaluate the resulting software, with a focus on development effort savings.

Spectral Clustering Effect in Software Development Effort Estimation

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.

Arahan Penyusunan dan Perancangan Masterplan Desa Wisata Madu Retno Berbasis Wisata Budaya

Madu Retno Village is the first transmigration village in the Batulicin area which was formed in 1980. Initially, village management was under the guidance of the Transmigration Department with a duration of 1 year. The village is growing but not maximal in its stages. Judging from its potential, Madu Retno Village has a strong Hindu socio-cultural character. In its development, the existing potential has not been mapped thoroughly so that there is no complete planning and development scheme, it does not yet have the direction of goals contained in the development scheme, the thematic growth direction, and indicators to improve adequate infrastructure. The overall urgency can be realized through the preparation of a village master plan which will be prepared with the residents so that they will find and appreciate the expectations of the residents. The concept of an independent village is a top priority in the preparation of this village master plan, then the concept of a cultural tourism village becomes the next development effort so that Madu Retno Village will develop tourism and community culture independently.