Published March 11, 2024 | Version v1
Dataset Open

Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset

  • 1. ROR icon Universidade da Coruña
  • 2. ROR icon Universidade de Santiago de Compostela

Description

There are four datasets:

1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.

2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.

3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.

The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.

Files

Balanced_promise_exp_nfr_train.csv

Files (282.0 kB)

Name Size Download all
md5:8142bc98aa77014c050f4b49aac43e01
137.1 kB Preview Download
md5:a0c30144af6e41d4af18118a757e9c92
760 Bytes Preview Download
md5:61385fdfe7a7f8e30c386bcce92fead8
20.9 kB Preview Download
md5:c4040e89fa0f5faf31180a1db35ea83b
123.3 kB Preview Download

Additional details

References

  • [1]Lima, M., Valle, V., Costa, E., Lira, F., & Gadelha, B. (2019, September). Software engineering repositories: expanding the promise database. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering (pp. 427-436).