Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring. Poza, J., Moreno, V., Fraga, A., & Álvarez-Rodríguez, J. M. Applied Sciences, 2021. Paper doi abstract bibtex The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods.
@Article{app112311378,
AUTHOR = {Poza, Jesús and Moreno, Valentín and Fraga, Anabel and Álvarez-Rodríguez, José María},
TITLE = {Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring},
JOURNAL = {Applied Sciences},
VOLUME = {11},
YEAR = {2021},
NUMBER = {23},
ARTICLE-NUMBER = {11378},
URL = {https://www.mdpi.com/2076-3417/11/23/11378},
ISSN = {2076-3417},
ABSTRACT = {The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods.},
DOI = {10.3390/app112311378}
}
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