Improving Requirements Specification in WebREd-Tool by Using a NFR's Classification

In Software Engineering (SE), a system has properties that emerge from the combination of its parts, these emergent properties will surely be a matter of system failure if the Non-Fuctional Requirements (NFRs), or system qualities, are not specified in advance. In Web Engineering (WE) field occurs very similar, but with some other issues related to special characteristics of the Web applications such as the navigation (with the application of the security). In this paper, we improve our Model-Driven tool, named WebREd-Tool, extending the requirements metamodel with a NFRs classification, the main idea is to help the Web application designer with the NFRs specification to make better design decisions and also to be used to validate the quality of the final Web application

Agile development in cloud computing for eliciting non-functional requirements

Agile is a popular and growing software development methodology. In the agile methodology, requirements are refined based on collaborations with customers and team members. However, the agile process faces a lack of visibility across the development and delivery processes, has complex and disjointed development processes and lacks communication agility between disconnected owners, development teams, and users. Furthermore, Non-Functional Requirements (NFR) are ignored due to the nature of agile development that lacks knowledge of the user and developer about NFR. In addition, extraction of the NFR is difficult and this difficulty is increased because the agile methodology promotes change in requirement at any stage of the development. Cloud computing services have helped solve some of the issues in the agile process. However, to address the issues in agile development, this research developed a framework for Agile Development in Cloud Computing (ADCC) that uses the facilitation of cloud computing to solve the above-mentioned issues. An Automated NFR eXtraction (ANFRX) method was developed to extract NFR from the software requirement documents and interview notes wrote during requirement gathering. The ANFRX method exploited the semantic knowledge of words in the requirement to classify and extract the NFR. Furthermore, an NFR Elicitation (NFRElicit) approach was developed to help users and development teams in elicitation of NFR in cloud computing. NFRElicit approach used components such as an organization’s projects history, ANFRX method, software quality standards, and templates. The ADCC framework was evaluated by conducting a case study and industrial survey. The results of the case study showed that the use of ADCC framework facilitated the agile development process. In addition, the industrial survey results revealed that the ADCC framework had a positive significant impact on communication, development infrastructure provision, scalability, transparency and requirement engineering activities in agile development. The ANFRX method was evaluated by applying it on PROMISE-NFR dataset. ANFRX method improved 40% and 26% in terms of f-measure from the Cleland and Slankas studies, respectively. The NFRElicit approach was applied to eProcurement dataset and evaluated in terms of more “Successful”, less “Partial Success” and “Failure” to identify NFR in requirement sentences. The NFRElicit approach improved 11.36% and 2.27% in terms of increase in “Successful” NFR, decrease of 5.68% and 1.14% in terms of “Partial success” and decrease of 5.68% and 1.13% in terms of “Failure” from the Non-functional requirement, Elicitation, Reasoning and Validation (NERV) and Capturing, Eliciting and Predicting (CEP) methodologies, respectively. The findings have shown the process was able to elicit and extract NFR for agile development in cloud computing

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

Aplicación de técnicas de minería de datos para extraer información de fuentes organizacionales, en la educación de requisitos

La ingeniería de requisitos tiene un papel importante en el éxito de un proyecto de software (Javed, 2010) a través de la educción, especificación, modelado y análisis de las necesidades planteadas por los Stakeholders sobre un producto de software (Unterkalmsteiner et al., 2015). La educación de requisitos dentro de la ingeniería de requisitos abarca el aprendizaje y la comprensión de las necesidades de los usuarios y los Stakeholders del proyecto, en aras de transmitirlas de una manera clara y concisa a los desarrolladores de software (Zowghi & Coulin, 2005) . Sin embargo, es importante resaltar que un usuario se centra en los RF del producto de software, dejando por fuera los RNF que imponen restricciones operativas en diferentes aspectos del comportamiento del sistema, según Mahmoud y Williams (2016).Magíster en Ingeniería de SoftwareMaestrí

an empirical study on classification of non-functional requirements

The classification of NKRs brings about the benefits that NKRs with respect to the same type In the system can be considered and Implemented aggregately by developers, and as a result be verified by quality assurers assigned for the type. This paper conducts an empirical study on using text mining techniques to classify NFRs automatically. Three kinds of Index terms, which are at different levels of llngulstlcal semantics, as Vgrams. Individual words, and multi-word expressions (MWE), are used In representation of NFRs. Then. SVM (Support Vector Machine) with linear kernel bt used as the classifier. We collected a data set from PROMISE web site for experimentation In this empirical study. The experiments show that Index term as Individual words with Boolean weighting outperforms the other two Index terms. When MWEs are used to enhance representation of Individual words, there Is no significant Improvement on classification performance. Automatic classification produces better performance on categories of large stees than that on categories of small sizes. It can be drawn from the experimental results that for automatic classification of NFRs. Individual words are the best Index terms In text representation of short NFRs' description and we should collect as many as possible NFRs of software system.Knowledge Systems Institute Graduate SchoolThe classification of NKRs brings about the benefits that NKRs with respect to the same type In the system can be considered and Implemented aggregately by developers, and as a result be verified by quality assurers assigned for the type. This paper conducts an empirical study on using text mining techniques to classify NFRs automatically. Three kinds of Index terms, which are at different levels of llngulstlcal semantics, as Vgrams. Individual words, and multi-word expressions (MWE), are used In representation of NFRs. Then. SVM (Support Vector Machine) with linear kernel bt used as the classifier. We collected a data set from PROMISE web site for experimentation In this empirical study. The experiments show that Index term as Individual words with Boolean weighting outperforms the other two Index terms. When MWEs are used to enhance representation of Individual words, there Is no significant Improvement on classification performance. Automatic classification produces better performance on categories of large stees than that on categories of small sizes. It can be drawn from the experimental results that for automatic classification of NFRs. Individual words are the best Index terms In text representation of short NFRs' description and we should collect as many as possible NFRs of software system