Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews

Autor(es)

Felizardo, Katia R. ; Salleh, Norsaremah ; Martins, Rafael M. ; Mendes, Emilia ; MacDonell, Stephen G. ; Maldonado, Jose C. ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2011 FIFTH INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2011); v. N/A, p. 10-pg., 2011-01-01.

Resumo

Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually. Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness. Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included. Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR. (AU)

• Processo FAPESP: 08/02024-6 - Evolução da ferramenta PEx para apoio à revisão sistemática
• Beneficiário: Rafael Messias Martins
• Modalidade de apoio: Bolsas no Brasil - Mestrado