Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation

Iris Reinhartz-Berger; Ilan Shimshoni; Aviva Abdal

doi:10.1007/978-3-030-21290-2_42

Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation

Iris Reinhartz-Berger, Ilan Shimshoni, Aviva Abdal

Department of Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings
Editors	Paolo Giorgini, Barbara Weber
Publisher	Springer Verlag
Pages	675-690
Number of pages	16
ISBN (Print)	9783030212896
DOIs	https://doi.org/10.1007/978-3-030-21290-2_42
State	Published - 2019
Event	31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 - Rome, Italy Duration: 3 Jun 2019 → 7 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11483 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	31st International Conference on Advanced Information Systems Engineering, CAiSE 2019
Country/Territory	Italy
City	Rome
Period	3/06/19 → 7/06/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Feature diagrams
Requirements specifications
Software Product Line Engineering
Variability analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-21290-2_42

Cite this

Reinhartz-Berger, I., Shimshoni, I., & Abdal, A. (2019). Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation. In P. Giorgini, & B. Weber (Eds.), Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings (pp. 675-690). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11483 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-21290-2_42

Reinhartz-Berger, Iris ; Shimshoni, Ilan ; Abdal, Aviva. / Behavior-Derived Variability Analysis : Mining Views for Comparison and Evaluation. Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. editor / Paolo Giorgini ; Barbara Weber. Springer Verlag, 2019. pp. 675-690 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Reinhartz-Berger, I , Shimshoni, I & Abdal, A 2019, Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation. in P Giorgini & B Weber (eds), Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11483 LNCS, Springer Verlag, pp. 675-690, 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019, Rome, Italy, 3/06/19. https://doi.org/10.1007/978-3-030-21290-2_42

Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation. / Reinhartz-Berger, Iris ; Shimshoni, Ilan; Abdal, Aviva.
Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. ed. / Paolo Giorgini; Barbara Weber. Springer Verlag, 2019. p. 675-690 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11483 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Reinhartz-Berger I , Shimshoni I, Abdal A. Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation. In Giorgini P, Weber B, editors, Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. Springer Verlag. 2019. p. 675-690. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-21290-2_42

Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this