Abstract
Feature Location is one of the most important and common activities performed by developers during software maintenance and evolution. In prior work, we show that Dynamic Feature Location obtains better results working with models rather than source code. In this work, we analyze how the criteria to create the model traces influence the Dynamic Feature Location results. We distinguish between two different criteria: configuration and architecture. Our Dynamic Feature Location approach is composed of dynamic analysis, information retrieval at the model trace level, and information retrieval at the model level. The evaluation in a Smart Hotel tests whether the traces created following the two criteria modify the results of the Feature Location by measuring recall, precision, and the combination of both (F-measure). The results reveal that in 75% of the cases the traces that follow the architecture criterion outperform the traces that follow the configuration criterion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alves, V., Schwanninger, C., Barbosa, L., Rashid, A., Sawyer, P., Rayson, P., Pohl, C., Rummler. A.: An exploratory study of information retrieval techniques in domain analysis. In: 2008 12th International Software Product Line Conference, pp. 67–76, September 2008
Antoniol, G., Gueheneuc, Y.-G.: Feature identification: an epidemiological metaphor. IEEE Trans. Softw. Eng. 32(9), 627–641 (2006)
Arcega, L., Font, J., Haugen, Ø., Cetina, C.: Feature location through the combination of run-time architecture models and information retrieval. In: Grabowski, J., Herbold, S. (eds.) SAM 2016. LNCS, vol. 9959, pp. 180–195. Springer, Cham (2016). doi:10.1007/978-3-319-46613-2_12
Basili, V.R.: The role of experimentation in software engineering: past, current, and future. In: Proceedings of the 18th International Conference on Software Engineering, ICSE 1996, pp. 442–449. IEEE Computer Society, Washington, DC (1996)
Basili, V.R., Caldiera, G., Rombach, H.D.: The goal question metric approach. In: Encyclopedia of Software Engineering. Wiley (1994)
Bencomo, N., Hallsteinsen, S., Santana de Almeida, E.: A view of the dynamic software product line landscape. Computer 45(10), 36–41 (2012)
Cetina, C.: Achieving autonomic computing through the use of variability models at run-time. Ph.D. thesis, Universidad Politécnica de Valencia (2010)
Dit, B., Revelle, M., Gethers, M., Poshyvanyk, D.: Feature location in source code: a taxonomy and survey. J. Softw. Maintenance Evol. Res. Pract. 25(1), 53–95 (2011)
Dit, B., Revelle, M., Poshyvanyk, D.: Integrating information retrieval, execution and link analysis algorithms to improve feature location in software. Empirical Softw. Eng. 18(2), 277–309 (2013)
Font, J., Arcega, L., Haugen, Ø., Cetina, C.: Building software product lines from conceptualized model patterns. In: Proceedings of the 2015 19th International Software Product Line Conference, SPLC 2015, Nashville, TN, USA (2015)
Font, J., Arcega, L., Haugen, Ø., Cetina, C.: Feature location in model-based software product lines through a genetic algorithm. In: Kapitsaki, G.M., Santana de Almeida, E. (eds.) ICSR 2016. LNCS, vol. 9679, pp. 39–54. Springer, Cham (2016). doi:10.1007/978-3-319-35122-3_3
Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, Stroudsburg, PA, USA, pp. 216–223. Association for Computational Linguistics (2003)
IBM: An architectural blueprint for autonomic computing. Technical report, IBM (2006)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998)
Lehman, M.M., Ramil, J., Kahen, G.: A paradigm for the behavioural modelling of software processes using system dynamics. Technical report, Imperial College of Science, Technology and Medicine, Department of Computing, September 2001
Liu, D., Marcus, A., Poshyvanyk, D., Rajlich, V.: Feature location via information retrieval based filtering of a single scenario execution trace. In: Proceedings of the Twenty-second IEEE/ACM International Conference on Automated Software Engineering, ASE 2007, New York, NY, USA, pp. 234–243. ACM (2007)
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
Martinez, J., Ziadi, T., Bissyandé, T.F., Le Traon, Y.: Bottom-up adoption of software product lines: a generic and extensible approach. In: Proceedings of the 19th International Software Product Line Conference, SPLC 2015, Nashville, TN, USA (2015)
Muñoz, J.: Model driven development of pervasive systems. building a software factory. Ph.D. thesis, Universidad Politécnica de Valencia (2008)
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)
Revelle, M., Dit, B., Poshyvanyk, D.: Using data fusion and web mining to support feature location in software. In: IEEE 18th International Conference on Program Comprehension (ICPC), pp. 14–23, June 2010
Salman, H.E., Seriai, A., Dony, C.: Feature location in a collection of product variants: combining information retrieval and hierarchical clustering. In: The 26th International Conference on Software Engineering and Knowledge Engineering, Hyatt Regency, Vancouver, BC, Canada, 1–3 July 2013, pp. 426–430 (2014)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Inc, New York (1986)
Travassos, M.O.B.G.H.: Contributions of in virtuo and in silico experiments for the future of empirical studies in software engineering. In: Proceedings of the Workshop on Empirical Studies in Software Engineering (ESEIW). IEEE Computer Society (2003)
Acknowledgments
This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF funds under the project Model-Driven Variability Extraction for Software Product Line Adoption (TIN2015-64397-R).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Arcega, L., Font, J., Haugen, Ø., Cetina, C. (2017). On the Influence of Models at Run-Time Traces in Dynamic Feature Location. In: Anjorin, A., Espinoza, H. (eds) Modelling Foundations and Applications. ECMFA 2017. Lecture Notes in Computer Science(), vol 10376. Springer, Cham. https://doi.org/10.1007/978-3-319-61482-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-61482-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61481-6
Online ISBN: 978-3-319-61482-3
eBook Packages: Computer ScienceComputer Science (R0)