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A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study

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A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study

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Blasco, D.; Cetina, C.; Pastor López, O. (2020). A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study. Information and Software Technology. 119:1-12. https://doi.org/10.1016/j.infsof.2019.106235

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/169015

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Title: A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study
Author: Blasco, Daniel Cetina, Carlos Pastor López, Oscar
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] Context:Commercial video games usually feature an extensive source code and requirements that are related to code lines from multiple methods. Traceability is vital in terms of maintenance and content update, so it ...[+]
Subjects: Requirement , Traceability , Evolutionary computation , Video game , Source code
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Information and Software Technology. (issn: 0950-5849 )
DOI: 10.1016/j.infsof.2019.106235
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.infsof.2019.106235
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096411-B-I00/ES/ASISTENTES EVOLUTIVOS INTELIGENTES PARA INICIAR LINEAS DE PRODUCTO SOFTWARE/
Thanks:
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 ALPS (RTI2018-096411-B-I00).
Type: Artículo

References

Watkins, R., & Neal, M. (1994). Why and how of requirements tracing. IEEE Software, 11(4), 104-106. doi:10.1109/52.300100

Rempel, P., & Mader, P. (2017). Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality. IEEE Transactions on Software Engineering, 43(8), 777-797. doi:10.1109/tse.2016.2622264

Borg, M., Runeson, P., & Ardö, A. (2013). Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering, 19(6), 1565-1616. doi:10.1007/s10664-013-9255-y [+]
Watkins, R., & Neal, M. (1994). Why and how of requirements tracing. IEEE Software, 11(4), 104-106. doi:10.1109/52.300100

Rempel, P., & Mader, P. (2017). Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality. IEEE Transactions on Software Engineering, 43(8), 777-797. doi:10.1109/tse.2016.2622264

Borg, M., Runeson, P., & Ardö, A. (2013). Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering, 19(6), 1565-1616. doi:10.1007/s10664-013-9255-y

Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259-284. doi:10.1080/01638539809545028

Poshyvanyk, D., Gueheneuc, Y.-G., Marcus, A., Antoniol, G., & Rajlich, V. (2007). Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval. IEEE Transactions on Software Engineering, 33(6), 420-432. doi:10.1109/tse.2007.1016

Dit, B., Revelle, M., Gethers, M., & Poshyvanyk, D. (2011). Feature location in source code: a taxonomy and survey. Journal of Software: Evolution and Process, 25(1), 53-95. doi:10.1002/smr.567

Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9

Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77-89. doi:10.1016/s0034-4257(97)00083-7

Apache opennlp: Toolkit for the processing of natural language text, 2017, (https://opennlp.apache.org/). [Online; accessed 12-November-2017].

P. Abeles, Efficient java matrix library, 2017, (http://ejml.org/). [Online; accessed 9-November-2017].

IGDA, International Game Developers Association, 2018.

Lucia, A. D., Fasano, F., Oliveto, R., & Tortora, G. (2007). Recovering traceability links in software artifact management systems using information retrieval methods. ACM Transactions on Software Engineering and Methodology, 16(4), 13. doi:10.1145/1276933.1276934

De Lucia, A., Oliveto, R., & Tortora, G. (2008). Assessing IR-based traceability recovery tools through controlled experiments. Empirical Software Engineering, 14(1), 57-92. doi:10.1007/s10664-008-9090-8

Zou, X., Settimi, R., & Cleland-Huang, J. (2009). Improving automated requirements trace retrieval: a study of term-based enhancement methods. Empirical Software Engineering, 15(2), 119-146. doi:10.1007/s10664-009-9114-z

Unterkalmsteiner, M., Gorschek, T., Feldt, R., & Lavesson, N. (2015). Large-scale information retrieval in software engineering - an experience report from industrial application. Empirical Software Engineering, 21(6), 2324-2365. doi:10.1007/s10664-015-9410-8

Bavota, G., De Lucia, A., Oliveto, R., & Tortora, G. (2014). Enhancing software artefact traceability recovery processes with link count information. Information and Software Technology, 56(2), 163-182. doi:10.1016/j.infsof.2013.08.004

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