Anabel Fraga

A methodology for the classification of quality of requirements using machine learning techniques

ContextOne of the most important factors in the development of a software project is the quality of their requirements. Erroneous requirements, if not detected early, may cause many serious problems, such as substantial additional costs, failure to meet the expected objectives and delays in delivery dates. For these reasons, great effort must be devoted in requirements engineering to ensure that the projects requirements results are of high quality. One of the aims of this discipline is the automatic processing of requirements for assessing their quality; this aim, however, results in a complex task because the quality of requirements depends mostly on the interpretation of experts and the necessities and demands of the project at hand. ObjectiveThe objective of this paper is to assess the quality of requirements automatically, emulating the assessment that a quality expert of a project would assess. MethodThe proposed methodology is based on the idea of learning based on standard metrics that represent the characteristics that an expert takes into consideration when deciding on the good or bad quality of requirements. Using machine learning techniques, a classifier is trained with requirements earlier classified by the expert, which then is used for classifying newly provided requirements. ResultsWe present two approaches to represent the methodology with two situations of the problem in function of the requirement corpus learning balancing, obtaining different results in the accuracy and the efficiency in order to evaluate both representations. The paper demonstrates the reliability of the methodology by presenting a case study with requirements provided by the Requirements Working Group of the INCOSE organization. ConclusionsA methodology that evaluates the quality of requirements written in natural language is presented in order to emulate the quality that the expert would provide for new requirements, with 86.1 of average in the accuracy.

Ontology-Assisted Systems Engineering Process with Focus in the Requirements Engineering Process

Problems found in the current Systems Engineering with focus in the Requirements Engineering Process shown that it could be improved using ontologies for aiding in the process. Requirements engineering in the Systems Engineering process is enhanced and quality of requirements enriched as well, improving Systems Engineering capabilities clearly can result in better Project Performance. One of that is the Requirement improvement and of course the benefit goes to the whole process of development. The more correct, complete and consistent it is, the best performance it will have and ontologies enable a more exhaustive and fast quality process.

Semantic Web or Web 2.0? Socialization of the Semantic Web

Web 2.0 and the Semantic Web are approaches that target the improvement of the Web through the optimization of mechanisms for sharing information and resources. This document argues that Web 2.0 is not an immature stage of the Semantic Web but an orthogonal dimension of another Web aspect, the semantic. Unfortunately, both dimensions are not independent; the more developed a semantic representation of a system is, in order to be more useful for the Semantic Web, the more distant it is to the Web 2.0. A semantic system highly formalized is less intuitive and less usable by users. For that reason, with the intention of avoiding the inverse relation between both dimensions, eight proposals are positioned and discussed in order to enable a real Social Semantic Web.

Training Initiative for New Software/Enterprise Architects: An Ontological Approach

In this paper, we describe the importance of new software/enterprise architects in the discipline of software architecture and enterprise architecture. Both are often idealized as super heroes with a lot of qualities that are very infrequent in contemporary people. The enterprise/software architect role could be assumed by a group of people able to manage the qualities for the role. In any case, even a group or a single person must be educated in the discipline by training courses, new methodologies of learning, or traditional university studies. In order to improve the process of becoming a new architect we propose a methodology based on ontological structures and reinforcement learning.

Knowledge Representation for Software Architecture Domain by Manual and Automatic Methodologies

At the moment, there is a need for new knowledge representation using Thesaurus or Ontologies because of the need to reuse knowledge. In this paper, a Software Architecture knowledge representation is created, for that purpose a manual and automatic methodology for creating it is used. A new manual methodology is provided in the paper. CAKE (Computer Aided Knowledge Environment) is the automatic process used as automatic methodology. The result is the first thesaurus in English for the Software Architecture Domain using the new manual methodology presented in the paper and the first ontology in Spanish for the Software Architecture Domain using the automatic methodology.

Extraction of Patterns Using NLP: Genetic Deafness.

In the domain of Genetic Deafness in medicine, it is important to detect some patterns. Medical doctors needs to search and deal with information from diverse sources and it is important to be able to cross information between sources. As part of a solution, one approach to minimize the impact of this lack and increase the success of the retrieval process crossing diverse sources of information laid in the use of Natural Language Processing techniques permitting conceptual integrity of text.

Eight steps towards the socialisation of the Semantic Web

The web presents an autonomous evolution that could be optimised having moved towards a more formalised semantic. Web 2.0 and the Semantic Web are approaches that target the improvement of the web through mechanisms for sharing information and resources. This document argues that Web 2.0 is not an immature stage of the Semantic Web but an orthogonal dimension of another web aspect, the semantic. Unfortunately, both dimensions are not independent; the more developed a semantic representation of a system is, for being more useful for the Semantic Web, the more distant it is to the Web 2.0. A semantic system highly formalised is less intuitive and less usable for users. The different possible evolutions are: the convergence of both webs, their coexistence and the autonomous evolution of the social web towards other semantic solutions. In order to enable a real Social Semantic Web, eight proposals are sited and discussed.

Automatic classification of web images as UML diagrams

Our purpose in this research is to develop a methodology to automatically and efficiently classify web images as UML static diagrams, and to produce a computer tool that implements this function. The tool receives as input a bitmap file (in different formats) and tells whether the image corresponds to a diagram. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19000 web images manually classified by experts. In this work we do not consider the textual contents of the images.

Syntactic-Semantic Extraction of Patterns Applied to the US and European Patents Domain.

Nowadays, there are many scientific inventions referring to any topic like medicine, technology, economics, finance, banking, computer science, and so on. These inventions are suggested as patents to the agencies working in US and Europe for the registration and revision of the patent applications. But, the job of reviewing the patents might be complicated because every day the quantity of it is bigger and bigger. And also, the amount of work dedicated writing a proper application might be intricate and needs several revisions from investor and examiners. This revision job might have costs for the inventor because they don’t know the proper language for writing the application in the formal mode used. As part of a solution, one approach to minimize the impact of this fact and increase the success of the reviewing process is aid the human reviewer and also inventors with a set of patterns created using Natural Language Processing techniques that accelerate the review just looking in the massive set of registration any similar one already patented and on the other hand aid the inventor writing in the formal manner the application.

Automatic Pattern Generator of Natural Language Text Applied in Public Health

At the moment, a huge amount of scientific articles is available, referring to a wide variety of topics like medicine, technology, economics, finance, and so on. Scientific papers show results of scientific interest and also present the evaluation and interpretation of relevant arguments. Due to the fact that these papers are created with a high frequency it is feasible to analyze how people write in a given domain. Within the discipline of natural language processing there are different approaches to analyze large amounts of text corpus. Identification patterns with semantic elements in a text, let us classify and examine the corpus to facilitate interpretation and management of information through computers. At the moment, a semiautomatic or automatic way to generate natural language patterns is not available or quite complicated. In the paper, it is shown how a tool developed for this research is tested in a domain of public health. The results obtained – by means of a tool and aided by graphs – provide groups of words that are used (to determine if they come from a specific vocabulary), most common grammatical categories, most repeated words in a domain, patterns found, and frequency of patterns found. A domain of public health has been selected containing 800 papers concerning different topics referring to genetics. The topics include mutations, genetic deafness, DNA, trinucleotide, suppressor genes, among others. An ontology of public health has been used to provide the basis of the study.