@InProceedings{FelizardoMenKalSouVij:2016:UsFoSn,
author = "Felizardo, Katia Romero and Mendes, Em{\'{\i}}lia and
Kalinowski, Marcos and Souza, {\'E}rica Ferreira and Vijaykumar,
Nandamudi Lankalapalli",
affiliation = "{Universidade Federal Tecnol{\'o}gico do Paran{\'a} (UFTPR)} and
{Blekinge Institute of Technology} and {Universidade Federal
Fluminense (UFF)} and {Universidade Federal Tecnol{\'o}gico do
Paran{\'a} (UFTPR)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Using forward snowballing to update systematic reviews in software
engineering",
booktitle = "Proceedings...",
year = "2016",
organization = "International Symposium on Empirical Software Engineering and
Measurement",
keywords = "forward snowballing, Systematic literature reviews.",
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.",
conference-location = "Ciudad Real, Espanha",
conference-year = "2016",
language = "en",
urlaccessdate = "28 abr. 2024"
}