Using visual text mining to support the study selection activity in systematic literature reviews

Felizardo, KR
Salleh, N
Martins, RM
Mendes, E
MacDonell, SG
Maldonado, JC
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IEEE Computer Society Press

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.

Evidence-based software engineering (EBSE), study selection activity, systematic literature review (SLR), visual text mining (VTM)
Proceedings of the 5th International Symposium on Empirical Software Engineering and Measurement, Banff, Canada, pages 26 - 35
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