Agile software development is a particularly intense knowledge activity in which the success depends greatly on the experience of the professionals involved in the process.Knowledge Management Strategies play an important role in assisting knowledge acquisition and sharing among Agile teams.In this scenario, this paper answer the following research question: What is the impact caused by the use of Agile practices in the process of organizational knowledge acquisition at software development companies?The objective is to analyze strategies for Knowledge Management among teams and evaluate the impact caused by the adoption of Agile practices on the Organizational Learning process.For this, we proposed a model which it was possible evaluate this impact.Thus, a survey was conducted with 455 respondents in order to validate the proposed model.The data collected in this research was processed and analyzed using Structural Equation Modeling.The results corroborates the impact of software development practices on Knowledge Management Strategies and Organizational Learning.Additionally, this study provides mechanisms for software engineering professionals to implement strategies that contribute to the knowledge acquisition and sharing in their teams.

Code smells or bad smells are an accepted approach to identify design flaws in the source code. Although it has been explored by researchers, the interpretation of programmers is rather subjective. One way to deal with this subjectivity is to use machine learning techniques. This paper provides the reader with an overview of machine learning techniques and code smells found in the literature, aiming at determining which methods and practices are used when applying machine learning for code smells identification and which machine learning techniques have been used for code smells identification. A mapping study was used to identify the techniques used for each smell. We found that the Bloaters was the main kind of smell studied, addressed by 35% of the papers. The most commonly used technique was Genetic Algorithms (GA), used by 22.22% of the papers. Regarding the smells addressed by each technique, there was a high level of redundancy, in a way that the smells are covered by a wide range of algorithms. Nevertheless, Feature Envy stood out, being targeted by 63% of the techniques. When it comes to performance, the best average was provided by Decision Tree, followed by Random Forest, Semi-supervised and Support Vector Machine Classifier techniques. 5 out of the 25 analyzed smells were not handled by any machine learning techniques. Most of them focus on several code smells and in general there is no outperforming technique, except for a few specific smells. We also found a lack of comparable results due to the heterogeneity of the data sources and of the provided results. We recommend the pursuit of further empirical studies to assess the performance of these techniques in a standardized dataset to improve the comparison reliability and replicability.

Computer Supported Cooperative Work (CSCW) is a research field focused on understanding characteristics of interdependent group work with the objective of designing adequate computer-based technology to support cooperative work processes. One of the key concepts of CSCW is the provision of relevant information to workers in a team, a concept named awareness. As the market and research community have already perceived the importance of providing fast and reliable information among team workers, it shares the interest of CSCW in awareness improvement. This addresses the following research question: What is the quality of awareness support in agile collaborative tools? To answer this question a survey was performed with 200 users which provided feedback scores for each given design element related to support of different awareness aspects. We used a Formal Technical Review (FTR) method specifically focused on awareness assessment, named Awareness Checklist. According to this method, there are 54 design elements that influence or contribute to awareness support. Those elements can be categorized in 14 design categories, which are directly related to six awareness types: Collaborations, Location, Context, Social, Workspace and Situation. We found that Microsoft Team Foundation Server, Jira and Trello offer more collaborative aware support, however about localization and context the DotProject tool obtained the highest score, as well in Social and the Situation too. The results offer the opportunity to assess the quality of awareness in any collaborative software used in small or bigger business projects and can be used to demonstrate certain aspects of the software which can be improved to achieve their user's satisfaction. The same concept can also be used to outline the tools main advantages and disadvantages, acting as a quality reviewer that can help to choose which collaborative tools should be adopted according to categories strengths and weaknesses.

In software engineering, the use of models and metamodeling approaches (e.g., MDA withMOF/UML) for purposes such as software design or software validation is an established practice. Ontologies constitute domain models formalized using expressive logic languages for class definitions and rules. Hence, when seen from an abstract point of view, the two paradigms and their various technological spaces seem closely related. However, in the state-of-the-art research and practice the two technologies are just beginning to converge and the relationship between the two is still under exploration. In this paper, we give an outline of current ontology technologies, such as the Semantic Web standards for a Web Ontology Language (OWL). Then, we describe a domain analysis of the different technical spaces, explaining the features of the different paradigms. Eventually, we describe some avenues for integrating various ontological technical spaces into metamodeling technical spaces.