TY - GEN
T1 - Cross product line analysis
AU - Wulf-Hadash, Ora
AU - Reinhartz-Berger, Iris
PY - 2013
Y1 - 2013
N2 - Due to increase in market competition and merger and acquisition of companies, different software product lines (SPLs) may exist under the same roof. These SPLs may be developed applying different domain analysis processes, but are likely not disjoint. Cross product line analysis aims to examine the common and variable aspects of different SPLs for improving maintenance and future development of related SPLs. Currently different SPL artifacts, or more accurately feature models, are compared, matched, and merged for supporting scalability, increasing modularity and reuse, synchronizing feature model versions, and modeling multiple SPLs for software supply chains. However, in all these cases the focus is on creating valid merged models from the input feature models. Furthermore, the terminology used in all the input feature models is assumed to be the same, namely similar features are named the same. As a result these methods cannot be simply applied to feature models that represent different SPLs. In this work we offer adapting similarity metrics and text clustering techniques in order to enable cross product line analysis. This way analysis of feature models that use different terminologies in the same domain can be done in order to improve the management of the involved SPLs. Preliminary results reveal that the suggested method helps systematically analyze the commonality and variability between related SPLs, potentially suggesting improvements to existing SPLs and to the maintenance of sets of SPLs.
AB - Due to increase in market competition and merger and acquisition of companies, different software product lines (SPLs) may exist under the same roof. These SPLs may be developed applying different domain analysis processes, but are likely not disjoint. Cross product line analysis aims to examine the common and variable aspects of different SPLs for improving maintenance and future development of related SPLs. Currently different SPL artifacts, or more accurately feature models, are compared, matched, and merged for supporting scalability, increasing modularity and reuse, synchronizing feature model versions, and modeling multiple SPLs for software supply chains. However, in all these cases the focus is on creating valid merged models from the input feature models. Furthermore, the terminology used in all the input feature models is assumed to be the same, namely similar features are named the same. As a result these methods cannot be simply applied to feature models that represent different SPLs. In this work we offer adapting similarity metrics and text clustering techniques in order to enable cross product line analysis. This way analysis of feature models that use different terminologies in the same domain can be done in order to improve the management of the involved SPLs. Preliminary results reveal that the suggested method helps systematically analyze the commonality and variability between related SPLs, potentially suggesting improvements to existing SPLs and to the maintenance of sets of SPLs.
KW - empirical evaluation
KW - feature clustering
KW - feature diagram matching
KW - feature diagram merging
KW - feature similarity
UR - http://www.scopus.com/inward/record.url?scp=84874010340&partnerID=8YFLogxK
U2 - 10.1145/2430502.2430531
DO - 10.1145/2430502.2430531
M3 - Conference contribution
AN - SCOPUS:84874010340
SN - 9781450315418
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 7th International Workshop on Variability Modelling of Software-Intensive Systems, VaMoS 2013
T2 - 7th International Workshop on Variability Modelling of Software-Intensive Systems, VaMoS 2013
Y2 - 23 January 2013 through 25 January 2013
ER -