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.

A dinâmica geográfica da construção de um território para moradias sociais: estudo testemunhal do empreendimento Vitória Brasil em Uberlândia (MG)

This work describes and analyzes the situation planned with the current, according to the housing development project style of social interest (Gleba 9), named Victory Brazil at Shopping Park II neighborhood, located in the southern sector of Uberlândia (MG), built with Program resources Minha Casa, Minha Vida (PMCMV). The specific objectives are: to present the housing policy of social interest directed to the Minha Casa, Minha Vida in the city of Uberlândia, from its history to its current information; trace the environmental characterization of the housing Vitoria Brazil through the development of thematic maps; identify the impacts generated by the housing projects of social interest; and show through interviews with 25 residents of Vitoria Brazil, the structural problems of the houses. Therefore, the research became quali-quantitative with descriptive approach and use of semi- structured interviews to collect data. In addition, the iconographic sources, ie photographs of the problems raised by the residents of the whole Victory Brazil and maps of the study area, enlarged the qualitative and dialogic nature of this study, as there is opportunity to contextualize specific way what was reported the residents interviewed. We understand that the city is intrinsically related to the design of agglomeration, characterized by the production of the possessor market capitalism a public administration system that is guided by the ideals of consumption and utilization of space, and its inhabitants are high class with the construction of equipment and adequate social services. One of the important biases to solving the problems of urbanization is by the intervention of the government through public policies through the master plan so that would allow prospects to enable the population of low-income housing in locations that offer housing and decent urbanization conditions.

Modelagem molecular voltada para a construção de um nanobiossensor para detecção do herbicida Imazaquin

In this study, our goal was develop and describe a molecular model of the enzyme-inhibiting interaction which can be used for an optimized projection of a Microscope Force Atomic nanobiosensor to detect pesticides molecules, used in agriculture, to evaluate its accordance with limit levels stipulated in valid legislation for its use. The studied herbicide (imazaquin) is a typical member of imidazolinone family and is an inhibitor of the enzymatic activity of Acetohydroxiacid Synthase (AHAS) enzyme that is responsible for the first step of pathway for the synthesis of side-chains in amino acids. The analysis of this enzyme property in the presence of its cofactors was made to obtain structural information and charge distribution of the molecular surface to evaluate its capacity of became immobilized on the Microscopy Atomic Force tip. The computational simulation of the system, using Molecular Dynamics, was possible with the force-field parameters for the cofactor and the herbicides obtained by the online tool SwissParam and it was implemented in force-field CHARMM27, used by software GROMACS; then appropriated simulations were made to validate the new parameters. The molecular orientation of the AHAS was defined based on electrostatic map and the availability of the herbicide in the active site. Steered Molecular Dynamics (SMD) Simulations, followed by quantum mechanics calculations for more representative frames, according to the sequential QM/MM methodology, in a specific direction of extraction of the herbicide from the active site. Therefore, external harmonic forces were applied with similar force constants of AFM cantilever for to simulate herbicide detection experiments by the proposed nanobiosensor. Force value of 1391 pN and binding energy of -14048.52 kJ mol-1 were calculated.