Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data
Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire developm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11127 |
| _version_ | 1827721892477272064 |
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| author | Fatih Yucalar |
| author_facet | Fatih Yucalar |
| author_sort | Fatih Yucalar |
| collection | DOAJ |
| description | Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual identification of these classes is a timewaster, and it needs to be automated. Methodically, machine learning (ML) approaches are applied to address this problem. In this study, twenty ML algorithms, such as Naïve Bayes, Rotation Forests, Convolutional Neural Networks, and transformers such as BERT, were used to predict FR and NFR. Any ML algorithm requires a dataset for training. For this goal, we generated a unique Turkish dataset having collected the requirements from real-world software projects with 4600 samples. The generated Turkish dataset was used to assess the performance of the three groups of ML algorithms in terms of F-score and related statistical metrics. In particular, out of 20 ML algorithms, BERTurk was found to be the most successful algorithm for discriminating FR and NFR in terms of a 95% F-score metric. From the FR and NFR identification problem point of view, transformer algorithms show significantly better performances. |
| first_indexed | 2024-03-10T21:29:05Z |
| format | Article |
| id | doaj.art-1d58fd96ab6049a3bd2c69026e74dd01 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:29:05Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1d58fd96ab6049a3bd2c69026e74dd012023-11-19T15:28:54ZengMDPI AGApplied Sciences2076-34172023-10-0113201112710.3390/app132011127Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish DataFatih Yucalar0Department of Software Engineering, Manisa Celal Bayar University, Manisa 45400, TurkeyRequirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual identification of these classes is a timewaster, and it needs to be automated. Methodically, machine learning (ML) approaches are applied to address this problem. In this study, twenty ML algorithms, such as Naïve Bayes, Rotation Forests, Convolutional Neural Networks, and transformers such as BERT, were used to predict FR and NFR. Any ML algorithm requires a dataset for training. For this goal, we generated a unique Turkish dataset having collected the requirements from real-world software projects with 4600 samples. The generated Turkish dataset was used to assess the performance of the three groups of ML algorithms in terms of F-score and related statistical metrics. In particular, out of 20 ML algorithms, BERTurk was found to be the most successful algorithm for discriminating FR and NFR in terms of a 95% F-score metric. From the FR and NFR identification problem point of view, transformer algorithms show significantly better performances.https://www.mdpi.com/2076-3417/13/20/11127software requirements classificationtransformer learningdeep neural networksmachine learningfunctional requirementsnon-functional requirements |
| spellingShingle | Fatih Yucalar Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data Applied Sciences software requirements classification transformer learning deep neural networks machine learning functional requirements non-functional requirements |
| title | Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data |
| title_full | Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data |
| title_fullStr | Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data |
| title_full_unstemmed | Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data |
| title_short | Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data |
| title_sort | developing an advanced software requirements classification model using bert an empirical evaluation study on newly generated turkish data |
| topic | software requirements classification transformer learning deep neural networks machine learning functional requirements non-functional requirements |
| url | https://www.mdpi.com/2076-3417/13/20/11127 |
| work_keys_str_mv | AT fatihyucalar developinganadvancedsoftwarerequirementsclassificationmodelusingbertanempiricalevaluationstudyonnewlygeneratedturkishdata |