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Erschienen in:
The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper) Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models: Approach and Industrial Evaluation

verfasst von : Ana C. Marcén, Francisca Pérez, Carlos Cetina

Erschienen in: Conceptual Modeling

Verlag: Springer International Publishing

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Abstract

In this work, we propose an evolutionary ontological encoding approach to enable Machine Learning techniques to be used to perform Software Engineering tasks in models. The approach is based on a domain ontology to encode a model and on an Evolutionary Algorithm to optimize the encoding. As a result, the encoded model that is returned by the approach can then be used by Machine Learning techniques to perform Software Engineering tasks such as concept location, traceability link retrieval, reuse, impact analysis, etc. We have evaluated the approach with an industrial case study to recover the traceability link between the requirements and the models through a Machine Learning technique (RankBoost). Our results in terms of recall, precision, and the combination of both (F-measure) show that our approach outperforms the baseline (Latent Semantic Indexing). We also performed a statistical analysis to assess the magnitude of the improvement.

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Vorheriges Kapitel On the Semantics of Ongoing and Future Occurrence Identifiers
Nächstes Kapitel The OntoREA© Accounting and Finance Model: Ontological Conceptualization of the Accounting and Finance Domain
Fußnoten
Literatur
1.
2.
3.
4.
Antoniol, G., Canfora, G., Casazza, G., De Lucia, A., Merlo, E.: Recovering traceability links between code and documentation. IEEE Trans. Softw. Eng. 28(10), 970–983 (2002)CrossRef
5.
Arcuri, A., Briand, L.: A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Softw. Test. Verif. Reliab. 24(3), 219–250 (2014)CrossRef
6.
Arcuri, A., Fraser, G.: Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Softw. Eng. 18(3), 594–623 (2013)CrossRef
7.
B Le, T.D., Lo, D., Le Goues, C., Grunske, L.: A learning-to-rank based fault localization approach using likely invariants. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 177–188. ACM (2016)
8.
9.
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 129–136. ACM, New York (2007)
10.
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)CrossRef
11.
12.
De Lucia, A., Fasano, F., Oliveto, R., Tortora, G.: Enhancing an artefact management system with traceability recovery features. In: Proceedings of 20th IEEE International Conference on Software Maintenance, pp. 306–315. IEEE (2004)
13.
14.
Eaddy, M., Aho, A., Murphy, G.C.: Identifying, assigning, and quantifying crosscutting concerns. In: Proceedings of the First International Workshop on Assessment of Contemporary Modularization Techniques, p. 2 (2007)
15.
Eaddy, M., Aho, A.V., Antoniol, G., Guéhéneuc, Y.G.: Cerberus: tracing requirements to source code using information retrieval, dynamic analysis, and program analysis. In: ICPC 2008 Conference, pp. 53–62. IEEE (2008)
16.
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4(Nov), 933–969 (2003)MathSciNetMATH
17.
Haiduc, S., Bavota, G., Oliveto, R., De Lucia, A., Marcus, A.: Automatic query performance assessment during the retrieval of software artifacts. In: International Conference on Automated Software Engineering, pp. 90–99. ACM (2012)
18.
Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C., Guisan, A.: Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199(2), 142–152 (2006)CrossRef
19.
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). doi:10.​1007/​BFb0026683CrossRef
20.
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, Stanford, CA, vol. 14, pp. 1137–1145 (1995)
21.
Marcus, A., Sergeyev, A., Rajlich, V., Maletic, J.: An information retrieval approach to concept location in source code. In: Proceedings of the 11th Working Conference on Reverse Engineering, pp. 214–223, November 2004
22.
Marcus, A., Maletic, J.I.: Recovering documentation-to-source-code traceability links using latent semantic indexing. In: Proceedings of 25th International Conference on Software Engineering, pp. 125–135. IEEE (2003)
23.
Navot, A., Shpigelman, L., Tishby, N., Vaadia, E.: Nearest neighbor based feature selection for regression and its application to neural activity. Adv. Neural Inf. Process. Syst. 18, 995 (2006)
24.
Poshyvanyk, D., Gueheneuc, Y.G., Marcus, A., Antoniol, G., Rajlich, V.: Feature location using probabilistic ranking of methods based on execution scenarios and information retrieval. IEEE Trans. Softw. Eng. 33(6), 420–432 (2007)CrossRef
25.
Sayyad, A.S., Ingram, J., Menzies, T., Ammar, H.: Scalable product line configuration: a straw to break the camel’s back. In: 2013 IEEE/ACM 28th International Conference on Automated Software Engineering (ASE), pp. 465–474, November 2013
26.
Shabtai, A., Moskovitch, R., Elovici, Y., Glezer, C.: Detection of malicious code by applying machine learning classifiers on static features: a state-of-the-art survey. Inf. Secur. Tech. Rep. 14(1), 16–29 (2009)CrossRef
27.
Svendsen, A., Zhang, X., Lind-Tviberg, R., Fleurey, F., Haugen, Ø., Møller-Pedersen, B., Olsen, G.K.: Developing a software product line for train control: a case study of CVL. In: Bosch, J., Lee, J. (eds.) SPLC 2010. LNCS, vol. 6287, pp. 106–120. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-15579-6_​8CrossRef
28.
Vargha, A., Delaney, H.D.: A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000)
29.
30.
Wolf, L., Martin, I.: Robust boosting for learning from few examples. In: Computer Vision and Pattern Recognition, vol. 1, pp. 359–364. IEEE (2005)
31.
Xuan, J., Monperrus, M.: Learning to combine multiple ranking metrics for fault localization. In: Proceedings of the 30th International Conference on Software Maintenance and Evolution (2014)
32.
Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)CrossRef
33.
Ye, X., Bunescu, R., Liu, C.: Learning to rank relevant files for bug reports using domain knowledge. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 689–699. ACM (2014)
34.
Ye, X., Bunescu, R., Liu, C.: Mapping bug reports to relevant files: a ranking model, a fine-grained benchmark, and feature evaluation. IEEE Trans. Softw. Eng. 42(4), 379–402 (2016)CrossRef
35.
Zisman, A., Spanoudakis, G., Pérez-Miñana, E., Krause, P.: Tracing software requirements artifacts. In: Software Engineering Research and Practice, pp. 448–455 (2003)
Metadaten
Titel
Ontological Evolutionary Encoding to Bridge Machine Learning and Conceptual Models: Approach and Industrial Evaluation
verfasst von
Ana C. Marcén
Francisca Pérez
Carlos Cetina
Copyright-Jahr
2017
Buch
Conceptual Modeling

Print ISBN: 978-3-319-69903-5

Electronic ISBN: 978-3-319-69904-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-69904-2_37