Towards an automatic classification for Spanish software requirements

3 pages•Published: February 16, 2023

María-Isabel Limaylla-Lunarejo, Nelly Condori-Fernández and Miguel Rodríguez Luaces

Abstract

Several machine learning (ML) algorithms in combination with natural language pro- cessing (NLP) techniques have been used in recent years in a promising way for the auto- matic classification of software requirements. Nevertheless, several works have focused on the English language. Due to the lack of work in the Spanish language, we performed a con- trolled experiment using ML algorithms in combination with text vectorization techniques to investigate the best combination for Spanish requirements classification. Based on f1- score metrics, we found the combination of SVM with TF-IDF performs better than other combinations, with a value of 0.95 for functional and 0.79 for non-functional classification.

Keyphrases: automatic classification requirements, machine learning, natural language processing, spanish requirements

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 124-126.

Links:	https://easychair.org/publications/paper/KFbq
	https://doi.org/10.29007/b8sk

BibTeX entry

@inproceedings{XoveTIC2022:Towards_automatic_classification_Spanish,
  author    = {María-Isabel Limaylla-Lunarejo and Nelly Condori-Fernández and Miguel Rodríguez Luaces},
  title     = {Towards an automatic classification for Spanish software requirements},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/KFbq},
  doi       = {10.29007/b8sk},
  pages     = {124-126},
  year      = {2023}}

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