Abstract
Depois de uma evolução constante, e mais recentemente, em apenas uma década, o uso da informação nas ciências, nas empresas e na sociedade se tem modificado profundamente. O objetivo desta comunicação é discutir as transformações do risco, com as novas zonas de sombra nos processos decisórios em relação à utilização massiva de dados e de ferramentas de inteligência artificia. Destas transformações, resultam em novas combinações no vida cotidiana e nas tomadas de decisões, nomeadamente em empresas e nos sistemas políticos e nos processos eleitorais.

Abstract
The increase in retention of customer in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system which uses the following pipeline (data collection, predictive model, loyalty actions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a second step, predictive models are applied to identify user profiles more susceptible to dropout. Actionable rules are generated based on actionable attributes that should be avoided, in order to increase retention. Finally, in the third step, based on the previous actionable knowledge, experimental planning is carried out, with test and control groups, in order to find the best loyalty actions for customer retention. This document presents a simulation and the measure of the business utility of an actions sequence to avoid dropout. © 2019, Springer Nature Switzerland AG.

Abstract
This work presents an actionable knowledge discovery system for real user needs with three steps. In the first step, it extracts and transforms existing data in the databases of the ERP and CRM systems of the sports facilities and loads them into a Data Warehouse. In a second phase, predictive models are applied to identify profiles more susceptible to abandonment. Finally, in the third phase, based on the previous models, experimental planning is carried out, with test and control groups, in order to find concrete actions for customer retention. © Springer Nature Switzerland AG 2019.

Abstract
Bug localization (BL) from the bug report is the strategic activity of the software maintaining process. Because BL is a costly and tedious activity, BL techniques information retrieval-based and machine learning-based could aid software engineers. We propose a method for BUg Localization with word embeddings and Network Regularization (BULNER). The preliminary results suggest that BULNER has better performance than two state-of-the-art methods.

Abstract
Software Operational Profile (SOP) is a software specification based on how users use the software. This specification corresponds to a quantitative representation of software that identifies the most used software parts. As software reliability depends on the context in which users operate the software, ones use SOP in software reliability engineering. However, there are evidence of a misalignment between the software tested parts and SOP. Therefore, this paper investigates a possible misalignment between SOP and the tested software parts to obtain, based on experimental data, more evidence of this misalignment. We performed an exploratory study composed of three activities to verify: a) whether there are significant variations in how users operate the software; b) whether there is a misalignment between SOP and the tested software parts; c) if failures occur in untested SOP parts in case of misalignment. To perform these verifications, we defined the term”test profile” and presented it in this paper. We instrumented three software to collect data from them while the users were operating this software. Posteriorly, we analyzed these collected data in an attempt to reach the goals of this paper. To evaluate the originality of this research, we performed a Literature Systematic Review (SLR) and presented its conclusions. The obtained results evidence that there are significant variations in how users operate the software and also that there was a misalignment between SOP and the tested software parts when we evaluated the three software mentioned above. There were also indications of the occurrence of failures in the untested SOP parts. These results indicate that SOP becomes relevant not only to software reliability engineering but also to contribute to testing activities, regardless of the adopted strategy.

Abstract
In the past few years, the literature has shown that the practice of reuse through requirement patterns is an effective alternative to address specification quality issues, with the additional benefit of time savings. Due to the interactions between requirements engineering and other phases of the software development life cycle (SDLC), these benefits may extend to the entire development process. This paper describes a revisited systematic literature mapping (SLM) that identifies and analyzes research that demonstrates those benefits from the use of requirement patterns for software design, construction, testing, and maintenance. In this extended version, the SLM protocol includes automatic search over two additional sources of information and the application of the snowballing technique, resulting in ten primary studies for analysis and synthesis. In spite of this new version of the SLM protocol, results still point out a small number of studies on requirement patterns at the SDLC (excluding requirements engineering). Results indicate that there is yet an open field for research that demonstrates, through empirical evaluation and usage in practice, the pertinence of requirement patterns at software design, construction, testing, and maintenance.