This research to practice full paper observed that graduate subjects (or courses) are commonly offered in graduate programs and can provide specialized knowledge of different topics that are important for the formation of Ph.D. and Master’s students. At the same time, Systematic Literature Review (SLR) has been increasingly adopted in the computing area as a research method to synthesize the state of the art of a given research topic, identify research groups working on that topic, understand the existing limitations and research gaps, and also identify new research directions. However, it is still not well understood the real benefits and challenges of offering a subject that addresses SLR for graduate students. Moreover, it is not known the difficulties faced by professors (i.e., educators) to teach this subject. The main goal of this paper is to present an experience report of teaching SLR, in particular, the benefits and challenges of this subject for computer science graduate students. In addition, this paper also presents the essential topics of SLR that we recommend to be taught and a better way to teach them, aiming at supporting graduate courses to offer it. For this, we surveyed computer science graduate students who attended the SLR subject that was taught for almost ten years in our institutions. In particular, we collected the lessons learned, findings, and insights; following, we summarized the benefits and challenges for students, the difficulties for professors, and also those essential topics to be taught. As a main result, the SLR subject can be considered a valuable opportunity for graduate students that could use this subject to conduct the required deep literature review of their research topic and have a better comprehension of their research area. Besides and more importantly, this subject can improve important research skills of students, including the ability to recognize research problems, analyze and synthesize data, think critically, and write papers. Therefore, we believe that graduate courses should analyze the possibility of offering the SLR subject.

Context: For thousands of years humans have been using natural language to register their knowledge on important information to enable its access to future generations. With internet, a large amount of textual data is produced and shared on a daily basis. So, scientists started to research techniques for efficiently process knowledge stored in textual format. In this context, Natural Language Processing (NLP) became a popular area studying linguistic phenomena and using computational methods to process texts in natural language. In particular, Open Information Extraction (Open IE) was proposed to gather information from plain text. Despite the advances in this area, it is still necessary to map details about how these approaches were proposed to support the community while creating more efficient Open IE systems. Objective: In this paper, we identify, in the literature, the main characteristics of proposed Open IE approaches. Method: First, we extended the search performed in a systematic mapping previously published by using backward snowballing and a manual search. Next, we updated the electronic database search including ACL Anthology. Finally, 159 studies proposing Open IE approaches were considered for data extraction. Results: Data analysis showed a significant increase in the number of studies published about Open IE in the last years. In addition, we provide important details about how these techniques were proposed (e.g., data sets used and output evaluation techniques). Results indicate that researchers started to adopt neural networks to perform Open IE instead of using conventional supervised learning techniques. Conclusion: Recent advances in Artificial Intelligence and neural networks techniques allowed scientists to have a new perspective on how to perform efficient textual data management. Therefore, Open IE approaches gained much attention as they can help in many contexts, especially in knowledge management tasks.

Background: The software engineering community has increasingly conducted systematic literature reviews (SLR) as a means to summarize evidence from different studies and bring to light the state of the art of a given research topic. While SLR provide many benefits, they also present several problems with punctual solutions for some of them. However, two main problems still remain: the high time-/effort-consumption nature of SLR and the lack of an effective impact of SLR results in the industry, as initially expected for SLR. Aims: The main goal of this paper is to introduce a new view - which we name Sustainability of SLR - on how to deal with SLR aiming at reducing those problems. Method: We analyzed six reference studies published in the last decade to identify, group, and analyze the SLR problems and their interconnections. Based on such analysis, we proposed the view of Sustainability of SLR that intends to address these problems. Results: The proposed view encompasses three dimensions (social, economic, and technical) that could become SLR more sustainable in the sense that the four major problems and 31 barriers (i.e., possible causes for those problems) that we identified could be mitigated. Conclusions: The view of Sustainability of SLR intends to change the researchers' mindset to mitigate the inherent SLR problems and, as a consequence, achieve sustainable SLR, i.e., those that consume less time/effort to be conducted and updated with useful results for the industry.

Context: Secondary studies, as Systematic Literature Reviews (SLRs) and Systematic Mappings (SMs), have been providing methodological and structured processes to identify and select research evidence in Computer Science, especially in Software Engineering (SE). One of the main activities of a secondary study process is to read the abstracts to decide on including or excluding studies. This activity is considered costly and time-consuming. In order to speed up the selection activity, some alternatives such as, structured abstracts and graphical abstracts (e.g. Concept Maps – CMs), have been proposed. Objective: This study presents an approach to automatically build CMs using Natural Language Processing (NLP) to support the selection activity of secondary studies. Method: First, we proposed an approach composed by two pipelines: (1) perform the triple extraction of concept-relation-concept based on NLP; and (2) attach the extracted triples in a structure used as a template to scientific studies. Second, we evaluated both pipelines conducting experiments. Results: The preliminary evaluation revealed that CMs extracted are coherent when compared with their source text. Conclusions: NLP can assist the automatic construction of CMs. In addition, the experiment results show that the approach can be useful to support researchers in the selection of studies in the selection activity of secondary studies.