Concept Maps Construction Using Natural Language Processing to Support Studies Selection
Published in 33rd ACM/SIGAPP Symposium on Applied Computing, 2018
Evidence-Based Software Engineering (EBSE) employs appropriate research methods to build a body of knowledge on Software Engineering (SE) practice. In this context, secondary studies, as Systematic Literature Reviews (SLRs) and Systematic Mappings (SMs), have been providing methodological and structured processes to identify and select relevant evidence. This first selection is usually conducted by just reading titles and abstracts of these studies. Besides being a time-consuming activity involving often a considerable number of studies, abstracts are many times not well-written and, as a consequence, this activity has usually required a significant amount of cost and effort. Recent literature has provided evidence that unstructured and poorly written abstracts may compromise the selection activity. One potential solution to minimize such problem is to promote the use of structured and graphical abstracts.
Recommended citation: SANTOS, V. (2018). "Concept Maps Construction Using Natural Language Processing to Support Studies Selection." In: Symposium On Applied Computing, Pau, França, Proceedings of the 33rd ACM/SIGAPP Symposium On Applied Computing, Pau, France, 2018.
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