RT Conference Proceedings
T1 A literature review and comparison of three feature location techniques using ArgoUML-SPL
A1 Cruz, Daniel
A1 Figueiredo, Eduardo
A1 Martinez, Jabier
AB Over the last decades, the adoption of Software Product Line (SPL) engineering for supporting software reuse has increased. An SPL can be extracted from one single product or from a family of related software products, and feature location strategies are widely used for variability mining. Several feature location strategies have been proposed in the literature and they usually aim to map a feature to its source code implementation. In this paper, we present a systematic literature review that identifies and characterizes existing feature location strategies. We also evaluated three different strategies based on textual information retrieval in the context of the ArgoUML-SPL feature location case study. In this evaluation, we compare the strategies based on their ability to correctly identify the source code of several features from ArgoUML-SPL ground truth. We then discuss the strengths and weaknesses of each feature location strategy.
PB Association for Computing Machinery
SN 9781450366489
YR 2019
FD 2019-02-06
LK https://hdl.handle.net/11556/1884
UL https://hdl.handle.net/11556/1884
LA eng
NO Cruz , D , Figueiredo , E & Martinez , J 2019 , A literature review and comparison of three feature location techniques using ArgoUML-SPL . in Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 . ACM International Conference Proceeding Series , Association for Computing Machinery , 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 , Leuven , Belgium , 6/02/19 . https://doi.org/10.1145/3302333.3302343
NO conference
NO Publisher Copyright: © 2019 Association for Computing Machinery.
NO This research was partially supported by Brazilian funding agencies: CNPq (Grant 424340/2016-0), CAPES, and FAPEMIG (grant PPM-00651-17).
DS TECNALIA Publications
RD 28 jul 2024