Ibero-American Consensus on Low- and No-Calorie Sweeteners: Safety, Nutritional Aspects and Benefits in Food and Beverages

International scientific experts in food, nutrition, dietetics, endocrinology, physical activity, paediatrics, nursing, toxicology and public health met in Lisbon on 2-4 July 2017 to develop a Consensus on the use of low- and no-calorie sweeteners (LNCS) as substitutes for sugars and other caloric sweeteners. LNCS are food additives that are broadly used as sugar substitutes to sweeten foods and beverages with the addition of fewer or no calories. They are also used in medicines, health-care products, such as toothpaste, and food supplements. The goal of this Consensus was to provide a useful, evidence-based, point of reference to assist in efforts to reduce free sugars consumption in line with current international public health recommendations. Participating experts in the Lisbon Consensus analysed and evaluated the evidence in relation to the role of LNCS in food safety, their regulation and the nutritional and dietary aspects of their use in foods and beverages. The conclusions of this Consensus were: (1) LNCS are some of the most extensively evaluated dietary constituents, and their safety has been reviewed and confirmed by regulatory bodies globally including the World Health Organisation, the US Food and Drug Administration and the European Food Safety Authority; (2) Consumer education, which is based on the most robust scientific evidence and regulatory processes, on the use of products containing LNCS should be strengthened in a comprehensive and objective way; (3) The use of LNCS in weight reduction programmes that involve replacing caloric sweeteners with LNCS in the context of structured diet plans may favour sustainable weight reduction. Furthermore, their use in diabetes management programmes may contribute to a better glycaemic control in patients, albeit with modest results. LNCS also provide dental health benefits when used in place of free sugars; (4) It is proposed that foods and beverages with LNCS could be included in dietary guidelines as alternative options to products sweetened with free sugars; (5) Continued education of health professionals is required, since they are a key source of information on issues related to food and health for both the general population and patients. With this in mind, the publication of position statements and consensus documents in the academic literature are extremely desirable

A Systematic Mapping Study on Empirical Evaluation of Software Requirements Specifications Techniques

This paper describes an empirical mapping study, which was designed to identify what aspects of Software Requirement Specifications (SRS) are empirically evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place

Requirements Classification Using FastText and BETO in Spanish Documents

Context and motivation: Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques have effectively supported the automatic software requirements classification. The emergence of pre-trained language models, like BERT, provides promising results in several downstream NLP tasks, such as text classification. Question/problem: Most ML/DL approaches on requirements classification show a lack of analysis for requirements written in the Spanish language. Moreover, there has not been much research on pre-trained language models, like fastText and BETO (BERT for the Spanish language), neither in the validation of the generalization of the models. Principal ideas/results: We aim to investigate the classification performance and generalization of fastText and BETO classifiers in comparison with other ML/DL algorithms. The findings show that Shallow ML algorithms outperformed fastText and BETO when training and testing in the same dataset, but BETO outperformed other classifiers on prediction performance in a dataset with different origins. Contribution: Our evaluation provides a quantitative analysis of the classification performance of fastTest and BETO in comparison with ML/DL algorithms, the external validity of trained models on another Spanish dataset, and the translation of the PROMISE NFR dataset in Spanish

Experimental Study Using Functional Size Measurement in Building Estimation Models for Software Project Size

This paper reports on an experiment that investigates the predictability of software project size from software product size. The predictability research problem is analyzed at the stage of early requirements by accounting the size of functional requirements as well as the size of non-functional requirements. The experiment was carried out with 55 graduate students in Computer Science from Concordia University in Canada. In the experiment, a functional size measure and a project size measure were used in building estimation models for sets of web application development projects. The results show that project size is predictable from product size. Further replications of the experiment are, however, planed to obtain more results to confirm or disconfirm our claim

Early Usability Measurement in Model-Driven Development: Definition and Empirical Evaluation

Usability is currently a key feature for developing quality systems. A system that satisfies all the functional requirements can be strongly rejected by end-users if it presents usability problems. End-users demand intuitive interfaces and an easy interaction in order to simplify their work. The first step in developing usable systems is to determine whether a system is or is not usable. To do this, there are several proposals for measuring the system usability. Most of these proposals are focused on the final system and require a large amount of resources to perform the evaluation (end-users, video cameras, questionnaires, etc.). Usability problems that are detected once the system has been developed involve a lot of reworking by the analyst since these changes can affect the analysis, design, and implementation phases. This paper proposes a method to minimize the resources needed for the evaluation and reworking of usability problems. We propose an early usability evaluation that is based on conceptual models. The analyst can measure the usability of attributes that depend on conceptual primitives. This evaluation can be automated taking as input the conceptual models that represent the system abstractly

Towards an automatic requirements classification in a new Spanish dataset

Machine Learning (ML) algorithms have become a powerful instrument in software requirements classification. Nevertheless, most of the research focusing on requirements is in English, with less attention to other languages. Given a lack of datasets in Spanish, we created a new dataset from a collection of requirements from final degree projects from the University of A Coruña. In this paper, we investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification in a Spanish dataset. We found that SVM with TF-IDF gives the highest f1-score (0.95 and 0.79 for functional and non-functional classification)

Assessing the Effect of Screen Mockups on the Comprehension of Functional Requirements

Over the last few years, the software engineering community has proposed a number of modeling methods to represent functional requirements. Among them, use cases are recognized as an easy to use and intuitive way to capture and define such requirements. Screen mockups (also called user-interface sketches or user interface-mockups) have been proposed as a complement to use cases to improve the comprehension of functional requirements. In this paper, we aim at quantifying the benefits achievable by augmenting use cases with screen mockups in the comprehension of functional requirements with respect to effectiveness, effort, and efficiency. For this purpose, we conducted a family of four controlled experiments, involving 139 participants having different profiles. The experiments involved comprehension tasks performed on the requirements documents of two desktop applications. Independently from the participants' profile, we found a statistically significant large effect of the presence of screen mockups on both comprehension effectiveness and comprehension task efficiency. While no significant effect was observed on the effort to complete tasks. The main "take away" lesson is that screen mockups are able to almost double the efficiency of comprehension task