Using Forward Snowballing to update Systematic Reviews in Software Engineering. Felizardo, K. R., Mendes, E., Kalinowski, M., de Souza, É. F., & Vijaykumar, N. L. In Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2016, Ciudad Real, Spain, September 8-9, 2016, pages 53:1-53:6, 2016. Best Short Paper Award!
Author version doi abstract bibtex 1 download Background: A Systematic Literature Review (SLR) is a methodology used to aggregate relevant evidence related to one or more research questions. Whenever new evidence is published after the completion of a SLR, this SLR should be updated in order to preserve its value. However, updating SLRs involves significant effort. Objective: The goal of this paper is to investigate the application of forward snowballing to support the update of SLRs. Method: We compare outcomes of an update achieved using the forward snowballing versus a published update using the search-based approach, i.e., searching for studies in electronic databases using a search string. Results: Forward snowballing showed a higher precision and a slightly lower recall. It reduced in more than five times the number of primary studies to filter however missed one relevant study. Conclusions: Due to its high precision, we believe that the use of forward snowballing considerably reduces the effort in updating SLRs in Software Engineering; however the risk of missing relevant papers should not be underrated. © 2016 ACM.
@inproceedings{FelizardoMKSV16,
author = {Katia Romero Felizardo and
Emilia Mendes and
Marcos Kalinowski and
{\'{E}}rica Ferreira de Souza and
Nandamudi L. Vijaykumar},
title = {Using Forward Snowballing to update Systematic Reviews in Software
Engineering},
abstract = {Background: A Systematic Literature Review (SLR) is a methodology used to aggregate relevant evidence related to one or more research questions. Whenever new evidence is published after the completion of a SLR, this SLR should be updated in order to preserve its value. However, updating SLRs involves significant effort. Objective: The goal of this paper is to investigate the application of forward snowballing to support the update of SLRs. Method: We compare outcomes of an update achieved using the forward snowballing versus a published update using the search-based approach, i.e., searching for studies in electronic databases using a search string. Results: Forward snowballing showed a higher precision and a slightly lower recall. It reduced in more than five times the number of primary studies to filter however missed one relevant study. Conclusions: Due to its high precision, we believe that the use of forward snowballing considerably reduces the effort in updating SLRs in Software Engineering; however the risk of missing relevant papers should not be underrated. © 2016 ACM.},
booktitle = {Proceedings of the 10th {ACM/IEEE} International Symposium on Empirical
Software Engineering and Measurement, {ESEM} 2016, Ciudad Real, Spain,
September 8-9, 2016},
pages = {53:1-53:6},
note = {<font color="red">Best Short Paper Award!</font>},
year = {2016},
urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/FelizardoMKSV16.pdf},
doi = {10.1145/2961111.2962630},
}
Downloads: 1
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