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.
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Notes
- 1.
SOVA refers to a fourth role – instrument – how is the action performed? Due to the absence of this part in our example, we exclude it from the discussion.
- 2.
Clustering is done as part of the third step in SOVA – variability analysis; the other part of this step – feature diagram generation – is not required for the current work.
- 3.
The requirements used for the evaluation can be found at https://sites.google.com/is.haifa.ac.il/corereq/tool-data/generated-outputs.
References
Assunção, W.K., Lopez-Herrejon, R.E., Linsbauer, L., Vergilio, S.R., Egyed, A.: Reengineering legacy applications into software product lines: a systematic mapping. Empirical Softw. Eng. 22(6), 2972–3016 (2017)
Bakar, N.H., Kasirun, Z.M., Salleh, N., Jalab, H.A.: Extracting features from online software reviews to aid requirements reuse. Appl. Soft Comput. 49, 1297 (2016)
Bakar, N.H., Kasirun, Z.M., Salleh, N., Jalab, H.A.: Feature extraction approaches from natural language requirements for reuse in software product lines: a systematic literature review. J. Syst. Softw. 106, 132–149 (2015)
Ben Nasr, S., et al.: Automated extraction of product comparison matrices from informal product descriptions. J. Syst. Softw. 124, 82–103 (2017)
Berger, T., et al.: A survey of variability modeling in industrial practice. In: Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems, p. 7. ACM, January 2013
Bonin, F., Dell’Orletta, F., Montemagni, S., Venturi, G.: A contrastive approach to multi-word extraction from domain-specific corpora. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010) (2010)
Clements, P., Northrop, L.: Software Product Lines: Practices and Patterns, vol. 3. Addison-Wesley, Reading (2002)
Davril, J.M., Delfosse, E., Hariri, N., Acher, M., Cleland-Huang, J., Heymans, P.: Feature model extraction from large collections of informal product descriptions. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 290–300. ACM, August 2013
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)
Ferrari, A., Spagnolo, G.O., Dell’Orletta, F.: Mining commonalities and variabilities from natural language documents. In: Proceedings of the 17th International Software Product Line Conference, pp. 116–120. ACM, August 2013
Itzik, N., Reinhartz-Berger, I.: SOVA-a tool for semantic and ontological variability analysis. In: CAiSE (Forum/Doctoral Consortium), pp. 177–184 (2014)
Itzik, N., Reinhartz-Berger, I.: Generating feature models from requirements: structural vs. functional perspectives. In: Proceedings of the 18th International Software Product Line Conference: Companion, Workshops, Demonstrations and Tools, vol. 2, pp. 44–51. ACM, September 2014
Itzik, N., Reinhartz-Berger, I., Wand, Y.: Variability analysis of requirements: considering behavioral differences and reflecting stakeholders’ perspectives. IEEE Trans. Softw. Eng. 42(7), 687–706 (2016)
Kang, K.C., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, A.S.: Feature-oriented domain analysis (FODA) feasibility study (No. CMU/SEI-90-TR-21). Software Engineering Institute, Carnegie-Mellon University, Pittsburgh, PA (1990)
Martinez, J., Ziadi, T., Bissyandé, T.F., Klein, J., Le Traon, Y.: Bottom-up adoption of software product lines: a generic and extensible approach. In: Proceedings of the 19th International Conference on Software Product Line, pp. 101–110. ACM, July 2015
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI, vol. 6, pp. 775–780, July 2006
Niu, N., Savolainen, J., Niu, Z., Jin, M., Cheng, J.R.C.: a systems approach to product line requirements reuse. IEEE Syst. J. 8(3), 827–836 (2014)
OMG: The Requirements Interchange Format Specification – Version 1.2. https://www.omg.org/spec/ReqIF/
Pohl, K., Böckle, G., van Der Linden, F.J.: Software Product Line Engineering: Foundations, Principles and Techniques. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-28901-1
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Reinhartz-Berger, I., Itzik, N., Wand, Y.: Analyzing variability of software product lines using semantic and ontological considerations. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 150–164. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_11
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, vol. 400, no. 1, pp. 525–526, August 2000
Steinley, D.: Properties of the Hubert-Arable adjusted Rand index. Psychol. Methods 9(3), 386 (2004)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics, June 1994
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Reinhartz-Berger, I., Shimshoni, I., Abdal, A. (2019). Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_42
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