Value-Based Fuzzy Approach for Non-functional Requirements Prioritization

Non-functional requirements (NFRs) are often addressed late in a project and, in turn, can get less attention in the requirements prioritization (RP) process. For various reasons, RP may happen based on functional requirements (FRs) only. While many approaches for prioritizing NFRs have been published, these are known also for some limitations, e.g. not being scalable, being domain-specific and not able to cope with changing requirements. In this paper, we proposes a value-based fuzzy approach for prioritizing NFRs together with FRs. Our proposed approach takes into account (1) the relationships of NFRs with FRs using experts’ evaluations and fuzzy logic, and (2) the dependencies among both types of requirements and also the interdependencies that particularly exist among the NFRs themselves. We evaluated our proposal by conducting a real-world case study of an ATM system. We also compared the list of prioritized NFRs with the list of NFRs prioritized by different stakeholders on the basis of classification factors. The results of applying the proposed approach on NFRs of ATM system show that the approach produces a conflict-free and consistent list of prioritized NFRs.</p

Optimisation Method for Training Deep Neural Networks in Classification of Non- functional Requirements

Non-functional requirements (NFRs) are regarded critical to a software system's success. The majority of NFR detection and classification solutions have relied on supervised machine learning models. It is hindered by the lack of labelled data for training and necessitate a significant amount of time spent on feature engineering. In this work we explore emerging deep learning techniques to reduce the burden of feature engineering. The goal of this study is to develop an autonomous system that can classify NFRs into multiple classes based on a labelled corpus. In the first section of the thesis, we standardise the NFRs ontology and annotations to produce a corpus based on five attributes: usability, reliability, efficiency, maintainability, and portability. In the second section, the design and implementation of four neural networks, including the artificial neural network, convolutional neural network, long short-term memory, and gated recurrent unit are examined to classify NFRs. These models, necessitate a large corpus. To overcome this limitation, we proposed a new paradigm for data augmentation. This method uses a sort and concatenates strategy to combine two phrases from the same class, resulting in a two-fold increase in data size while keeping the domain vocabulary intact. We compared our method to a baseline (no augmentation) and an existing approach Easy data augmentation (EDA) with pre-trained word embeddings. All training has been performed under two modifications to the data; augmentation on the entire data before train/validation split vs augmentation on train set only. Our findings show that as compared to EDA and baseline, NFRs classification model improved greatly, and CNN outperformed when trained using our suggested technique in the first setting. However, we saw a slight boost in the second experimental setup with just train set augmentation. As a result, we can determine that augmentation of the validation is required in order to achieve acceptable results with our proposed approach. We hope that our ideas will inspire new data augmentation techniques, whether they are generic or task specific. Furthermore, it would also be useful to implement this strategy in other languages

Using NLP to generate user stories from software specification in natural language

Orientador: Andrey Ricardo PimentelDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/08/2018Inclui referências: p. 80-82Resumo: O processo de elicitar as Histórias de Usuário requeridas para o desenvolvimento de software exige tempo e dedicação, e pode apresentar muito retrabalho se as conversas com partes interessadas não fornecerem informações coesas. O principal problema enfrentado é que o cliente muitas vezes não tem clareza sobre o que ele realmente quer, e no estado da arte não havia uma abordagem ou ferramenta que auxiliasse a transpor o que cliente deseja em histórias de usuário. Pensando nisso, propusemos a abordagem e ferramenta UserStoryGen para simplificar todo esse trabalho extenso, resolvendo esse problema através do uso de técnicas de Linguagem Natural (NLP) com estruturas adequadas para esse propósito e usando o modelo padrão de descrição de história para gerar automaticamente histórias de usuários. A abordagem UserStoryGen consiste em extrair informações como: título, descrição, verbo principal, usuários e entidades sistêmicas de histórias de usuários, a partir do texto não estruturado. O UserStoryGen usa o texto big picture como entrada para processamento de texto e geração automática de histórias do usuário. As histórias do usuário são geradas por meio de uma Restful A P I no formato JSON e podem ser exibidas tanto nesse formato, se apenas a chamada da Restful API for usada, como usando uma interface gráfica que mostrará o resultado em uma tabela. A implementação da UserStoryGen teve como objetivo automatizar este processo trabalhoso de extração de histórias de usuários do texto e obteve resultados significativos, principalmente nos testes com dados da indústria. Entre os três grupos de estudos de caso realizados, o terceiro, que utilizou dados da indústria, obteve os melhores resultados com textos que tiveram uma acurácia média de 76%, precisão de 88,23%, recall de 78,95% e medida F1 de 83,33%. O segundo grupo de estudos de casos com textos fornecidos por especialistas em Engenharia de Software obteve uma acurácia média de 73,68%, precisão de 85,71% e F1 de 82,76%. O primeiro grupo, utilizando textos de umwhite paper e de um livro teve o pior resultado, com uma acurácia média de 60% e uma medida F1 de 60,87%. Com base nos resultados obtidos com a UserStoryGen, concluímos que é completamente possível atingir o objetivo se pré-identificar e extrair as possíveis histórias de usuário para um determinado texto, e a implementação da abordagem proposta também pode ser melhorada em trabalhos futuros. A UserStoryGen representa um ganho para o Processo de Desenvolvimento Ágil, eliminando o tempo gasto na identificação de Estórias de Usuário, quando a equipe possui um texto com a big picture ou um documento textual das funcionalidades para usar como entrada. Palavras-chave: Processamento de Linguagem Natural, Extração Automática, Histórias de Usuário, Stanford CoreNLP.Abstract: The process of eliciting User Stories required for software development requires both time and dedication, and can present a lot of rework if conversations with stakeholders do not provide cohesive information. The main problem faced is that the client often lacks clarity about what he really wants, and in the state of the art there was no approach or tool that helps transpose what the customer wants into user stories. Thinking on it, we proposed the UserStoryGen approach and tool to simplify all this extensive work, resolving the issue through the use of Natural Language Processing (NLP) techniques with structures and the standard user story description template to automatically generate user stories. UserStoryGen's approach consists of extracting information such as: title, description, main verb, users and systemic entities of user stories from the unstructured text. The UserStoryGen uses big picture text as input for text processing and automated generation of the user stories. The user stories are generated through a Restful API in the JSON format and can be viewed either in this format, if only the Restful API call is used, as well as using a graphic interface that shows the results through a table. The implementation of UserStoryGen is aimed to automate the laborious process of extracting user stories from text and it obtained significant results, mainly with industry data. Among the three groups of case studies, the third one, that used industry data, obtained the best results with texts that had an average accuracy of 76%, precision of 88.23%, recall of 78.95% and F1 measure of 83.33%. The second group, using texts provided by software engineering specialists obtained an average accuracy of 73.68%, precision of 85.71% and F1 measure of 82.76%. The first group, using texts from a white paper and a book had the worst results with an average accuracy of 60% and a F1 measure of 60.87%. Based in the results obtained with the UserStoryGen, we concluded that it's completely possible to achieve the goal if pre-identifying and extracting the possible user stories for a given text, and the implementation of the proposed approach also can be improved in the future works. The UserStoryGen is a gain for Agile Development Process by eliminating time spent in User Stories identification when the team has a big picture text or a Features textual document to use as input. Keywords: Natural Language Processing, Automatic Extraction, User Stories, Stanford CoreNLP