A Design-Oriented Specification Language for defining software requirements

The most common difficulty of requirements elicitation is to define all components and aspects of a project by using the natural language without ambiguities, weak phrases and useless descriptions which make project outlines chaotic. In this paper, we present the Design Oriented Specification Language (DOSLang) that aims at reducing the gap existing among the project stakeholders, which are involved into the requirements specification and comprehension activities. The DOSLang language provides a free-form syntax with the introduction of constructs helping project stakeholders to reducing the ambiguity during definitions, descriptions, todos, actions, constraints and all the other aspects related to requirement definition, without sacrificing the ability of specifying loops and conditional constructs, small sets of data type and multiplicity between 'entities'. The DOSLang compiler is designed to be able to create a common baseline, which the project stakeholders can use during the first phases of the project elicitation. DOSLang is suitable for any kind of project that requires a precise description of all of its aspects. Moreover the language was created keeping the possibility to be used by all people working in a project development: from business people to programmers, including customers

Gherkin Specification Extension : uma linguagem de especificação de requisitos baseada em Gherkin

Orientador: Andrey Ricardo PimentelDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 28/02/2019Inclui referências: p.68-72Área de concentração: Ciência da ComputaçãoResumo: O desenvolvimento de software e composto de varias fases, entre elas esta a fase de elicitacao, negociacao e validacao de requisitos. Nesta fase, geralmente utiliza-se linguagem natural para definir e negociar os requisitos do sistema que sera desenvolvido. Entretanto, as linguagens naturais podem ser ambiguas, dificultando o entendimento do requisito, e portanto a sua negociacao e validacao. Uma das tentativas de solucionar o problema da ambiguidade foi a criacao de linguagens de especificacao de requisitos. Algumas destas linguagens, como Z e Alneelain, utilizam metodos formais na definicao dos requisitos. O problema da utilizacao de metodos formais e que todos que lerao os requisitos devemter conhecimentos em metodos formais, fato que nem sempre e verdade, ja que alguns interessados pelo software, como clientes e analistas de negocios, podem nao ter este conhecimento. Outras linguagens, como i* e KAOS, utilizam elementos graficos para especificar um sistema. Este formato pode complicar o entendimento, pois diagramas grandes tem alta complexidade cognitiva. Para tentar solucionar este problema, e apresentado o Gherkin Specification Extension (GSE), uma extensao da linguagem Gherkin, que utiliza linguagem natural em conjunto de estruturas fixas para reduzir o problema da ambiguidade durante a especificacao de requisitos utilizando linguagem natural. O GSE foi estruturado com base em onze requisitos que definem sete secoes diferentes (Descricao, Grupo, Restricoes e qualidades, Relacionamento, Planejamento, Metricas e Notas). Este requisitos foram estudados de forma a identificar funcionalidades chave para melhorar a comunicacao entre diversos interessados no software, seja ele um desenvolvedor, gerente de projetos, cliente, usuario ou analista de negocios. A extensao foi validada quanto a sua aceitacao com pessoas com poucos conhecimentos em desenvolvimento de software, obtendo resultado positivos referente a qualidade da especificacao gerada e aceitacao da tecnologia. Para mensurar a aceitacao da tecnologia foi utilizado o modelo TAM (Technology Acceptance Model) em sua terceira versao. Palavras-chave: Linguagem, Especificacao, Requisitos.Abstract: Software development consists of several phases, including elicitation, negotiation and validation of requirements. At this stage, natural language is usually used to define and negotiate the system requirements that will be developed. However, natural languages can be ambiguous, making it difficult to understand the requirement, and therefore its negotiation and validation. One of the attempts to solve the ambiguity problem was the creation of requirements specification languages. Some of these languages, such as Z and Alneelain, use formal methods in defining requirements. The problem with using formal methods is that everyone who will read the requirements must have knowledge of formal methods, a fact that is not always true, as some software stakeholders, such as customers and business analysts, may not have this knowledge. Other languages, such as i * and KAOS, use graphics to specify a system. This format may complicate understanding, since very large diagrams generally have high cognitive complexity. In order to solve this problem, the Gherkin Specification Extension (GSE), an extension of the Gherkin language, is presented, which uses natural language in conjunction with fixed structures to reduce the problem of ambiguity during specification of requirements using natural language . The GSE was structured based on eleven requirements that define seven different sections (Description, Group, Constraints and Qualities, Relationship, Planning, Metrics and Notes). These requirements have been studied in order to identify key functionalities to improve communication among diverse stakeholders in the software, be it a developer, project manager, client, user or business analyst. The extension was validated for its acceptance with people with little knowledge in software development, obtaining positive results regarding the quality of the generated specification and acceptance of the technology. To measure the acceptance of the technology, the TAM (Technology Acceptance Model) model was used in its third version. Keywords: Language, Specification, Requirement

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions