Construção de um classificador automático de severidade de bugs para sistemas open source

Software bug analysis is one of the most important activities in Software Quality. The rapid and correct implementation of the necessary repair influence both developers, who must leave the fully functioning software, and users, who need to perform their daily tasks. In this context, if there is an incorrect classification of bugs, there may be unwanted situations. One of the main factors to be assigned bugs in the act of its initial report is severity, which lives up to the urgency of correcting that problem. In this scenario, we identified in datasets with data extracted from five open source systems (Apache, Eclipse, Kernel, Mozilla and Open Office), that there is an irregular distribution of bugs with respect to existing severities, which is an early sign of misclassification. In the dataset analyzed, exists a rate of about 85% bugs being ranked with normal severity. Therefore, this classification rate can have a negative influence on software development context, where the misclassified bug can be allocated to a developer with little experience to solve it and thus the correction of the same may take longer, or even generate a incorrect implementation. Several studies in the literature have disregarded the normal bugs, working only with the portion of bugs considered severe or not severe initially. This work aimed to investigate this portion of the data, with the purpose of identifying whether the normal severity reflects the real impact and urgency, to investigate if there are bugs (initially classified as normal) that could be classified with other severity, and to assess if there are impacts for developers in this context. For this, an automatic classifier was developed, which was based on three algorithms (Näive Bayes, Max Ent and Winnow) to assess if normal severity is correct for the bugs categorized initially with this severity. The algorithms presented accuracy of about 80%, and showed that between 21% and 36% of the bugs should have been classified differently (depending on the algorithm), which represents somewhere between 70,000 and 130,000 bugs of the dataset.

Uma estratégia para otimizar a geração de arranjos em subestações virtuais de energia elétrica

The Virtual Reality techniques applied in Electricity Environments provide a new supervisory control paradigm. The fact of existing a virtual environment (VE), geometrically similar to a real substation, reduces the difference of mental models built by field operators compared with those built by system center operation improving the communication. Beside this, those systems can be used as visualization interfaces for electricity system simulators, training systems for professors and undergraduate students, field operators and maintenance professionals. However, the development process of these systems is quite complex, combining several activities such as implementation, 3D modeling, virtual sceneries construction, usability assessment and management project techniques. In this context, this work present a GUI strategy to build field arrangements based on scene graphs, to reduce time in Virtual Electricity Substations Arrangement development. Through this, mistakes during the VE building can be avoided making this process more reliable. As an concept proof, all toolkits developed in this work were applied in the virtualization of the substations from a Brazilian power concessionary named CEMIG.

Docência Universitária: o professor agrônomo na construção de sua professoralidade

The aim of this study was to understand how does the teaching learning process of the agronomy professor from Universidade Federal de Uberlândia occurs, analyzing the relationship among training, knowledge and these professors professional identity. The questions which guided the research were: How does the professional identity of these professors are developed in the teaching learning process? Which are the main formation factors that influences this process? Which are the agronomy professors knowledge and how were them built and learned by these professionals in college? This is a research with a qualitative approach, in which most of the data was collected with the professors, through two questionnaires, a simplified and an in-depth one, as well as documents that depict the history of the institute and of the Agronomy department. To guide analysis, we have used, more than the scientific production in the area, the authors Cunha (2008), Tardif (2002), Pimenta (2005), Melo (2009, 2012), Malusá (2005), Pachane (2009), Magalhães (2012), Dubar (2005), Imbernón (2002),Nunez andRamalho (2008), Almeida andPimenta (2012) PimentaandAnastasiou (2002), Cavallet (1999) Pachane (2006), Behrens (2007) Isaia (2008), Malusá (2012) and Nóvoa (1999). The analysis indicated that the teaching learning process of the agronomy professor occurs since college, with the option of been a professor, and throughout their teaching career. Their challenges are related to work conditions, although, the dilemmas between the practices related to the conceptions of traditional and progressive pedagogy can be seen in their class, teaching and teaching learning process conception. Classes are considered by professors as an interaction place but also as a knowledge transference place. This research identifies the necessity of promote cooperation between teachers, so that, the collective and integrated work in formative spaces teachers will have the opportunity to develop the knowledge necessary for teaching. The implementation of institutional initial and continuous formation, through direct dialogue between undergraduate, graduate and training and professional development programs for teachers, with better work conditions will reflect in the constant improvement of training development within universities.