Haris, M. Syauqi and Kurniawan, Tri Astoto and Ramdani, Fatwa (2020) AUTOMATED FEATURES EXTRACTION FROM SOFTWARE REQUIREMENTS SPECIFICATION (SRS) DOCUMENTS AS THE BASIS OF SOFTWARE PRODUCT LINE (SPL) ENGINEERING. vol. 40, no. 1, pp. 67–82, 2014., 40 (1). pp. 67-82. ISSN 2540-9433; E-ISSN 2540-9824
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Abstract
Extractive Software Product Line Engineering (SPLE) puts features on the foremost aspect in domain analysis that needs to be extracted from the existing system's artifact. Feature in SPLE, which is closely related to system functionality, has been previously studied to be extracted from source code, models, and various text documents that exist along the software development process. Source code, with its concise and normative standard, has become the most focused target for feature extraction source on many kinds of research. However, in the software engineering principle, the Software Requirements Specification (SRS) document is the basis or main reference for system functionality conformance. Meanwhile, previous studies of feature extraction from text document are conducted on a list of functional requirement sentences that have been previously prepared, not literally SRS as a whole document. So, this research proposes direct processing on the SRS document that uses requirement boilerplates for requirement sentence statement. The proposed method uses Natural Language Processing (NLP) approach on the SRS document. Sequence Part-of-Speech (POS) tagging pattern is used for automatic requirement sentence identification and extraction. The features are acquired afterward from extracted requirement sentences automatically using the word dependency parsing rules. Besides, mostly the previous studies about feature extraction were using non-public available SRS document that remains classified or not accessible, so this work uses selected SRS from publicly available SRS dataset to add reproducible research value. This research proves that requirement sentence extraction directly from the SRS document is viable with the precision value from 64% to 100% and recall value from 64% to 89%. While features extraction from extracted requirement sentences has a success rate from 65% to 88%.
| Item Type: | Article | ||||||||||||
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| Uncontrolled Keywords: | Software Product Line, Feature Extraction, Natural Language Processing | ||||||||||||
| Divisions: | Informatics Study Program | ||||||||||||
| Depositing User: | Yacobus Sudaryono | ||||||||||||
| Date Deposited: | 08 Jul 2022 08:41 | ||||||||||||
| Last Modified: | 04 Sep 2023 03:08 | ||||||||||||
| URI: | http://repository.itsk-soepraoen.ac.id/id/eprint/709 |
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