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Building a Grammar : Generating samples for Weighted Attribute Grammars.
Wolters, N.H.J. (2025) Building a Grammar : Generating samples for Weighted Attribute Grammars.
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| Abstract: | Grammars are vital in the development of grammarware tooling, where a large set of generated grammars can aid in the development and testing of their applications. However, the existing methodologies of generating new grammars are insufficient to generate a high amount and variety of these grammars. Where either a high number of language samples, positive and negative, are needed to generate a similar grammar in the form of grammar inference, or the implementation of non-CFG features is too hard to achieve. This thesis will research four state-of-the-art generative techniques: Grammarinator, XSmith, transformer networks, and evolutionary grammars. A comparison will be made on both the quality and the quantity of the artefacts, and using two different meta-grammars to differentiate results. The conclusion is that both transformer networks and evolutionary grammar are the most interesting techniques to perform further research, providing either some semantic similarity to meta-grammar or a high number/level of grammatically correct artefacts. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science MSc (60300) |
| Link to this item: | https://purl.utwente.nl/essays/106280 |
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