TY - GEN
T1 - Behavior-Derived Variability Analysis
T2 - 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019
AU - Reinhartz-Berger, Iris
AU - Shimshoni, Ilan
AU - Abdal, Aviva
N1 - Publisher Copyright: © 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The large variety of computerized solutions (software and information systems) calls for a systematic approach to their comparison and evaluation. Different methods have been proposed over the years for analyzing the similarity and variability of systems. These methods get artifacts, such as requirements, design models, or code, of different systems (commonly in the same domain), identify and calculate their similarities, and represent the variability in models, such as feature diagrams. Most methods rely on implementation considerations of the input systems and generate outcomes based on predefined, fixed strategies of comparison (referred to as variability views). In this paper, we introduce an approach for mining relevant views for comparison and evaluation, based on the input artifacts. Particularly, we equip SOVA – a Semantic and Ontological Variability Analysis method – with data mining techniques in order to identify relevant views that highlight variability or similarity of the input artifacts (natural language requirement documents). The comparison is done using entropy and Rand index measures. The method and its outcomes are evaluated on a case of three photo sharing applications.
AB - The large variety of computerized solutions (software and information systems) calls for a systematic approach to their comparison and evaluation. Different methods have been proposed over the years for analyzing the similarity and variability of systems. These methods get artifacts, such as requirements, design models, or code, of different systems (commonly in the same domain), identify and calculate their similarities, and represent the variability in models, such as feature diagrams. Most methods rely on implementation considerations of the input systems and generate outcomes based on predefined, fixed strategies of comparison (referred to as variability views). In this paper, we introduce an approach for mining relevant views for comparison and evaluation, based on the input artifacts. Particularly, we equip SOVA – a Semantic and Ontological Variability Analysis method – with data mining techniques in order to identify relevant views that highlight variability or similarity of the input artifacts (natural language requirement documents). The comparison is done using entropy and Rand index measures. The method and its outcomes are evaluated on a case of three photo sharing applications.
KW - Feature diagrams
KW - Requirements specifications
KW - Software Product Line Engineering
KW - Variability analysis
UR - http://www.scopus.com/inward/record.url?scp=85067344889&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-21290-2_42
DO - https://doi.org/10.1007/978-3-030-21290-2_42
M3 - Conference contribution
SN - 9783030212896
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 675
EP - 690
BT - Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings
A2 - Giorgini, Paolo
A2 - Weber, Barbara
PB - Springer Verlag
Y2 - 3 June 2019 through 7 June 2019
ER -