Typically, textual information is available as unstructured data, which require processing so that data mining algorithms can handle such data; this processing is known as the pre-processing step in the overall text mining process. This paper aims at analyzing the strong impact that the pre-processing step has on most mining tasks. Therefore, we propose a methodology to vary distinct combinations of pre-processing steps and to analyze which pre-processing combination allows high precision. In order to show different combinations of pre-processing methods, experiments were performed by comparing some combinations such as stemming, term weighting, term elimination based on low frequency cut and stop words elimination. These combinations were applied in text and opinion mining tasks, from which correct classification rates were computed to highlight the strong impact of the pre-processing combinations. Additionally, we provide graphical representations from each pre-processing combination to show how visual approaches are useful to show the processing effects on document similarities and group formation (i.e., cohesion and separation).

Software Quality Assurance is a sub-process that ensures that developed software meets and complies with defined or standardized quality specifications. Focusing on source code, there are characteristics that can be used to evaluate the quality. Introductory courses must encourage freshmen students to improve internal quality of their source code, but only as sophomore they have contact with Software Engineering concepts, including Quality Assurance. In this paper we present a tool to source code quality evaluation aimed at supporting students to improve their source code and, consequently, their programming skills. The proposed tool uses quality reports (available to professional environment integrate with software repositories) to analyze students' source code and provide a feedback about the student coding. The proposed tool run locally, with few computational resources. In addition, we proposed the methodology to use the proposed tool: it consists of challenging students to perform a set of maintenance tasks in a controlled environment. We prepared a source code by introducing common defects, what decreases the quality of source code, and ask to students to perform maintenance tasks in order to both eliminate the introduced defects and introduce new features. After each modification, the students must evaluate their code using the proposed tool to obtain a feedback about quality of source code. To evaluate the approach and the tool, we created a survey and applied to students and the teacher. As a result, we show the benefits of using the proposed tool to both teachers and students perspectives. The results are positive to enhance the teaching-learning Software Quality Assurance to Software Engineering students.

This research full paper presents a study on graduates' knowledge gaps in software testing according to industry needs. Several studies indicate that students graduate from computing programs with a knowledge gap in software testing. In this sense, we aimed to investigate in details this broader testing gap, by considering gaps in the level of testing topics. We conducted a survey with Brazilian practitioners in order to collect data (N=90). For each testing topic, knowledge gaps were calculated as the difference between what respondents' learned/practiced in undergraduate courses and what they actually applied in industry after graduating. Results provide evidence on points that could be improved in software testing education. Firstly, for all testing topics there was a negative gap on practice activities. This means that students could benefit from more testing assignments throughout the curriculum. Regarding gaps in concepts, some testing topics presented negative gaps (such as test in web applications) and others positive (such as test in aspect oriented software and mutation analysis). Therefore, results suggest that it is possible to counterbalance them in order to reduce the existing gaps. We also present respondents' opinions about their experience in software testing education and industry practices.

Formal languages and automata (FLA) theory have fundamental relevance to the base of knowledge in the computer science area, especially focusing on scientific education. Usually presented by a discipline, the teaching-learning process of FLA is characterized by the high level of abstraction, and it is considered difficult due to the complexity of language formalisms. As support for the learning process, tools have been used to simulate language formalisms. However, the simulation is not enough to reinforce the construction of an abstract concept. In this paper, we present an FLA teaching-learning methodology based on the development of simulators as an approach to clarify the formalism for the students. Through developing their simulators, students are exposed to the data structure and algorithms to handle the formalism. Consequently, students have the opportunity to make the concept concrete.