2 resultados para Lace bug

em Universidade Federal de Uberlândia


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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.

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Welding is one of the most employed process for joining steel pipes. Although, manual welding is still the most used one, mechanized version and even automatized one have increased its demand. Thus, this work deals with girth welding of API 5L X65 pipes with 8” of nominal diameter and 8.0 mm thickness, beveled with V-30º narrow gap. Torch is moved by a bug carrier (mechanized welding) and further the parameters are controlled as a function of angular position (automatized welding). Welding parameters are presented for filling the joint with two-passes (root and filling/capping passes). Parameters for the root pass were extracted from previous author´s work with weldments carried out in plates, but validated in this work for pipe welding. GMAW processes were assessed with short-circuit metal transfer in both conventional and derivative modes using different technologies (RMD, STT and CMT). After the parameter determination, mechanical testing was performed for welding qualification (uniaxial tension, face and root bending, nick break, Charpy V-notch impact, microhardness and macrograph). The initially obtained results for RMD and CMT were acceptable for all testing and, in a second moment, also for the STT. However, weld beads carried out by using the conventional process failed and revealed the existence of lack of fusion, which required further parametrization. Thus, a Parameter-Variation System for Girth Welding (SVP) was designed and built to allow varying the welding parameters as a function of angular position by using an inclinometer. The parameters were set for each of the three angular positions (flat, vertical downhill and overhead). By using such equipment and approach, the conventional process with parameter variation allowed reducing the welding time for joint accomplishment of the order of 38% for the root pass and 30% for the filling/capping pass.