Towards an automatic requirements classification in a new Spanish dataset

Maria Isabel Limaylla-Lunarejo; Nelly Condori-Fernandez; Miguel R. Luaces

doi:10.1109/RE54965.2022.00039

Towards an automatic requirements classification in a new Spanish dataset

Maria Isabel Limaylla-Lunarejo, Nelly Condori-Fernandez, Miguel R. Luaces

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

Abstract

Machine Learning (ML) algorithms have become a powerful instrument in software requirements classification. Nevertheless, most of the research focusing on requirements is in English, with less attention to other languages. Given a lack of datasets in Spanish, we created a new dataset from a collection of requirements from final degree projects from the University of A Coruña. In this paper, we investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification in a Spanish dataset. We found that SVM with TF-IDF gives the highest f1-score (0.95 and 0.79 for functional and non-functional classification).

Original language	English
Title of host publication	2022 IEEE 30th International Requirements Engineering Conference (RE)
Subtitle of host publication	[Proceedings]
Editors	Eric Knauss, Gunter Mussbacher, Chetan Arora, Muneera Bano, Jean-Guy Schneider
Publisher	IEEE Computer Society
Pages	270-271
Number of pages	2
ISBN (Electronic)	9781665470001
ISBN (Print)	9781665470018
DOIs	https://doi.org/10.1109/RE54965.2022.00039
Publication status	Published - 19 Oct 2022
Event	30th IEEE International Requirements Engineering Conference, RE 2022 - Virtual, Online, Australia Duration: 15 Aug 2022 → 19 Aug 2022

Publication series

Name	Proceedings of the IEEE International Conference on Requirements Engineering
Number	August
Volume	2022
ISSN (Print)	1090-705X
ISSN (Electronic)	2332-6441

Conference

Conference	30th IEEE International Requirements Engineering Conference, RE 2022
Country/Territory	Australia
City	Virtual, Online
Period	15/08/22 → 19/08/22

Bibliographical note

Funding Information:
Acknowledgments Partially funded by Xunta de Galicia/FEDER-UE GRC: ED431C 2021/53.

Publisher Copyright:
© 2022 IEEE.

Funding

Acknowledgments Partially funded by Xunta de Galicia/FEDER-UE GRC: ED431C 2021/53.

Keywords

automatic classification requirements
machine learning algorithms
natural language processing
Spanish requirements

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10.1109/RE54965.2022.00039

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Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022). Towards an automatic requirements classification in a new Spanish dataset. In E. Knauss, G. Mussbacher, C. Arora, M. Bano, & J.-G. Schneider (Eds.), 2022 IEEE 30th International Requirements Engineering Conference (RE): [Proceedings] (pp. 270-271). (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2022, No. August). IEEE Computer Society. https://doi.org/10.1109/RE54965.2022.00039

Limaylla-Lunarejo, Maria Isabel ; Condori-Fernandez, Nelly ; Luaces, Miguel R. / Towards an automatic requirements classification in a new Spanish dataset. 2022 IEEE 30th International Requirements Engineering Conference (RE): [Proceedings]. editor / Eric Knauss ; Gunter Mussbacher ; Chetan Arora ; Muneera Bano ; Jean-Guy Schneider. IEEE Computer Society, 2022. pp. 270-271 (Proceedings of the IEEE International Conference on Requirements Engineering; August).

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title = "Towards an automatic requirements classification in a new Spanish dataset",

abstract = "Machine Learning (ML) algorithms have become a powerful instrument in software requirements classification. Nevertheless, most of the research focusing on requirements is in English, with less attention to other languages. Given a lack of datasets in Spanish, we created a new dataset from a collection of requirements from final degree projects from the University of A Coru{\~n}a. In this paper, we investigate which combinations of text vectorization techniques with ML algorithms perform best for requirements classification in a Spanish dataset. We found that SVM with TF-IDF gives the highest f1-score (0.95 and 0.79 for functional and non-functional classification).",

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note = "Funding Information: Acknowledgments Partially funded by Xunta de Galicia/FEDER-UE GRC: ED431C 2021/53. Publisher Copyright: {\textcopyright} 2022 IEEE.; 30th IEEE International Requirements Engineering Conference, RE 2022 ; Conference date: 15-08-2022 Through 19-08-2022",

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Limaylla-Lunarejo, MI, Condori-Fernandez, N & Luaces, MR 2022, Towards an automatic requirements classification in a new Spanish dataset. in E Knauss, G Mussbacher, C Arora, M Bano & J-G Schneider (eds), 2022 IEEE 30th International Requirements Engineering Conference (RE): [Proceedings]. Proceedings of the IEEE International Conference on Requirements Engineering, no. August, vol. 2022, IEEE Computer Society, pp. 270-271, 30th IEEE International Requirements Engineering Conference, RE 2022, Virtual, Online, Australia, 15/08/22. https://doi.org/10.1109/RE54965.2022.00039

Towards an automatic requirements classification in a new Spanish dataset. / Limaylla-Lunarejo, Maria Isabel; Condori-Fernandez, Nelly; Luaces, Miguel R.
2022 IEEE 30th International Requirements Engineering Conference (RE): [Proceedings]. ed. / Eric Knauss; Gunter Mussbacher; Chetan Arora; Muneera Bano; Jean-Guy Schneider. IEEE Computer Society, 2022. p. 270-271 (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2022, No. August).

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

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Limaylla-Lunarejo MI, Condori-Fernandez N, Luaces MR. Towards an automatic requirements classification in a new Spanish dataset. In Knauss E, Mussbacher G, Arora C, Bano M, Schneider JG, editors, 2022 IEEE 30th International Requirements Engineering Conference (RE): [Proceedings]. IEEE Computer Society. 2022. p. 270-271. (Proceedings of the IEEE International Conference on Requirements Engineering; August). Epub 2022 Aug 15. doi: 10.1109/RE54965.2022.00039

Towards an automatic requirements classification in a new Spanish dataset

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Keywords

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