Hybrid Strategies

Copyright (c) 2018 Universidade Federal Fluminense (UFF).

A hybrid search strategy is an approach that combines database search and snowballing to conduct a Systematic Literature Review (SLR) in Software Engineering.

The goal of this work is to propose and evaluate hybrid search strategies combining searching in several databases or in a specific digital library with parallel or sequential backward and forward snowballing.

The proposed hybrid search strategies were applied to three previously published SLRs that adopted database searches and snowballing. For this, we used the Snowballing Tool developed by Joao Felipe Pimentel in Python. Additionally, we developed scripts to simulate the proposed hybrid search strategies.

We evaluated whether the strategys are able to retrieve the selected papers with lower effort in terms of the number of analyzed papers (Precision), whether they are able to retrieve all papers that were selected in the published SLR (Recall) and we used the harmonic mean between Precision and Recall (F-measure) to analyses the combined results.

To improve understanding, we adopted visualizations to represent the strategies flow, the citation matrix, citation graph, and the steps to find papers through backward and forward snowballing.

This work is part of Erica Mourao master thesis.

Experiments

Publications

• Mourão, E., Kalinowski, M., Murta, L., Mendes, E., & Wohlin, C.. Investigating the use of a hybrid search strategy for systematic reviews. ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2017 (pp. 193-198).

Team

The main team is composed by researchers from Universidade Federal Fluminense (UFF) and Pontifical Catholic University of Rio de Janeiro (PUC-Rio) in Brazil:

• Erica Mourao (UFF, Brazil)
• Joao Felipe Pimentel (UFF, Brazil)
• Leonardo Murta (UFF, Brazil)
• Marcos Kalinowski (PUC-Rio, Brazil)

Collaborators:

• Emilia Mendes (BTH, Sweden)
• Claes Wohlin (BTH, Sweden)

Included/Used Software

• Python
• Snowballing Tool

Acknowledgements

We would like to thank CAPES, CNPq and FAPERJ for partially supporting this work.

License Terms

The MIT License (MIT)

Copyright (c) 2018 Universidade Federal Fluminense (UFF).

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